aio-full.obo

format-version: 1.2
data-version: https://w3id.org/aio/releases/2024-11-11/aio-full.owl
subsetdef: aio:ActivationFunctionSubset "Activation Function Subset"
subsetdef: aio:BiasSubset "Bias Subset"
subsetdef: aio:ClassSubset "Class Subset"
subsetdef: aio:FunctionSubset "Function Subset"
subsetdef: aio:InstanceNormalizationLayerSubset "Instance Normalization Layer Subset"
subsetdef: aio:LayerSubset "Layer Subset"
subsetdef: aio:MachineLearningSubset "Machine Learning Subset"
subsetdef: aio:ModelSubset "Model Subset"
subsetdef: aio:NetworkSubset "Network Subset"
subsetdef: aio:PreprocessingSubset "Preprocessing Subset"
idspace: aio https://w3id.org/aio/ 
idspace: dcterms http://purl.org/dc/terms/ 
idspace: oboInOwl http://www.geneontology.org/formats/oboInOwl# 
ontology: https://w3id.org/aio/aio-full.owl
property_value: dcterms:description "This ontology models classes and relationships describing deep learning networks, their component layers and activation functions, as well as potential biases." xsd:string
property_value: dcterms:license http://creativecommons.org/licenses/by/4.0/
property_value: dcterms:title "Artificial Intelligence Ontology" xsd:string
property_value: owl:versionInfo "2024-11-11" xsd:string

[Term]
id: aio:AbstractRNNCell
name: AbstractRNNCell
def: "A layer representing an RNN cell that is the base class for implementing RNN cells with custom behavior." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AbstractRNNCell]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:ActivationLayer
name: Activation Layer
def: "A layer that applies an activation function to an output." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Activation]
comment: Applies an activation function to an output.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:ActiveLearning
name: Active Learning
def: "A machine learning task focused on methods that interactively query a user or another information source to label new data points with the desired outputs." [https://en.wikipedia.org/wiki/Active_learning_(machine_learning)]
subset: aio:MachineLearningSubset
synonym: "Query Learning" EXACT []
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:ActivityBias
name: Activity Bias
def: "A use and interpretation bias occurring when systems/platforms get training data from their most active users rather than less active or inactive users." [https://en.wikipedia.org/wiki/Interpretive_bias]
subset: aio:BiasSubset
is_a: aio:UseAndInterpretationBias ! Use And Interpretation Bias

[Term]
id: aio:ActivityRegularizationLayer
name: ActivityRegularization Layer
def: "A regularization layer that applies an update to the cost function based on input activity." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ActivityRegularization]
subset: aio:LayerSubset
is_a: aio:RegularizationLayer ! Regularization Layer

[Term]
id: aio:AdaptiveAvgPool1DLayer
name: AdaptiveAvgPool1D Layer
def: "A pooling layer that applies a 1D adaptive average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AdaptiveAvgPool1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AdaptiveAvgPool2DLayer
name: AdaptiveAvgPool2D Layer
def: "A pooling layer that applies a 2D adaptive average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AdaptiveAvgPool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AdaptiveAvgPool3DLayer
name: AdaptiveAvgPool3D Layer
def: "A pooling layer that applies a 3D adaptive average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AdaptiveAvgPool3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AdaptiveMaxPool1DLayer
name: AdaptiveMaxPool1D Layer
def: "A pooling layer that applies a 1D adaptive max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AdaptiveMaxPool1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AdaptiveMaxPool2DLayer
name: AdaptiveMaxPool2D Layer
def: "A pooling layer that applies a 2D adaptive max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AdaptiveMaxPool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AdaptiveMaxPool3DLayer
name: AdaptiveMaxPool3D Layer
def: "A pooling layer that applies a 3D adaptive max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AdaptiveMaxPool3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AddLayer
name: Add Layer
def: "A merging layer that adds a list of inputs taking as input a list of tensors all of the same shape." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Add]
comment: Layer that adds a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
subset: aio:LayerSubset
is_a: aio:MergingLayer ! Merging Layer

[Term]
id: aio:AdditionLayer
name: Addition Layer
def: "A layer that adds inputs from one or more other layers to cells or neurons of a target layer." []
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:AdditiveAttentionLayer
name: AdditiveAttention Layer
def: "An attention layer that implements additive attention also known as Bahdanau-style attention." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AdditiveAttention]
comment: Additive attention layer, a.k.a. Bahdanau-style attention.
subset: aio:LayerSubset
is_a: aio:AttentionLayer ! Attention Layer

[Term]
id: aio:AlphaDropoutLayer
name: AlphaDropout Layer
def: "A regularization layer that applies Alpha Dropout to the input keeping mean and variance of inputs to ensure self-normalizing property." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AlphaDropout]
comment: Applies Alpha Dropout to the input. Alpha Dropout is a Dropout that keeps mean and variance of inputs to their original values, in order to ensure the self-normalizing property even after this dropout. Alpha Dropout fits well to Scaled Exponential Linear Units by randomly setting activations to the negative saturation value.
subset: aio:LayerSubset
is_a: aio:RegularizationLayer ! Regularization Layer

[Term]
id: aio:AmplificationBias
name: Amplification Bias
def: "A processing bias arising when the distribution over prediction outputs is skewed compared to the prior distribution of the prediction target." [https://royalsocietypublishing.org/doi/10.1098/rspb.2019.0165#d1e5237]
subset: aio:BiasSubset
is_a: aio:ProcessingBias ! Processing Bias

[Term]
id: aio:AnchoringBias
name: Anchoring Bias
def: "An individual bias characterized by the influence of a reference point or anchor on decisions leading to insufficient adjustment from that anchor point." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:AnnotatorReportingBias
name: Annotator Reporting Bias
def: "An individual bias occurring when users rely on automation as a heuristic replacement for their own information seeking and processing." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:ArtificialNeuralNetwork
name: Artificial Neural Network
def: "A network based on a collection of connected units called artificial neurons modeled after biological neurons." [https://en.wikipedia.org/wiki/Artificial_neural_network]
comment: An artificial neural network (ANN) is based on a collection of connected units or nodes called artificial neurons, modeled after biological neurons, with connections transmitting signals processed by non-linear functions.
subset: aio:NetworkSubset
synonym: "ANN" EXACT []
synonym: "NN" EXACT []
is_a: aio:Network ! Network

[Term]
id: aio:AssociationRuleLearning
name: Association Rule Learning
def: "A supervised learning focused on a rule-based approach for discovering interesting relations between variables in large databases." [https://en.wikipedia.org/wiki/Association_rule_learning]
subset: aio:MachineLearningSubset
is_a: aio:SupervisedLearning ! Supervised Learning

[Term]
id: aio:AttentionLayer
name: Attention Layer
def: "A layer that implements dot-product attention also known as Luong-style attention." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Attention]
comment: Dot-product attention layer, a.k.a. Luong-style attention.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:AutoEncoderNetwork
name: Auto Encoder Network
def: "An unsupervised pretrained network that learns efficient codings of unlabeled data by training to ignore insignificant data and regenerate input from encoding." [https://en.wikipedia.org/wiki/Autoencoder]
comment: Layers: Input, Hidden, Matched Output-Input
subset: aio:NetworkSubset
synonym: "AE" EXACT []
is_a: aio:UnsupervisedPretrainedNetwork ! Unsupervised Pretrained Network
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:MatchedInputOutputLayer ! has part Matched Input-Output Layer

[Term]
id: aio:AutomationComplacencyBias
name: Automation Complacency Bias
def: "An individual bias characterized by over-reliance on automated systems leading to attenuated human skills." [https://doi.org/10.6028/NIST.SP.1270]
comment: Over-reliance on automated systems, leading to attenuated human skills, such as with spelling and autocorrect.
subset: aio:BiasSubset
synonym: "Automation Complaceny" EXACT []
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:AutoregressiveConditionalHeteroskedasticity
name: Autoregressive Conditional Heteroskedasticity
def: "A model that describes the variance of the current error term as a function of the previous periods' error terms, capturing volatility clustering. Used for time series data." []
subset: aio:ModelSubset
synonym: "ARCH" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:AutoregressiveDistributedLag
name: Autoregressive Distributed Lag
def: "A model that includes lagged values of both the dependent variable and one or more independent variables, capturing dynamic relationships over time. Used in time series analysis." []
subset: aio:ModelSubset
synonym: "ARDL" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:AutoregressiveIntegratedMovingAverage
name: Autoregressive Integrated Moving Average
def: "A model which combines autoregression (AR), differencing (I), and moving average (MA) components. Used for analyzing and forecasting time series data." []
subset: aio:ModelSubset
synonym: "ARIMA" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:AutoregressiveLanguageModel
name: Autoregressive Language Model
def: "A language model that generates text sequentially predicting one token at a time based on the previously generated tokens excelling at natural language generation tasks by modeling the probability distribution over sequences of tokens." []
subset: aio:ModelSubset
synonym: "generative language model" RELATED []
synonym: "sequence-to-sequence model" RELATED []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:AutoregressiveMovingAverage
name: Autoregressive Moving Average
def: "A model that combines autoregressive (AR) and moving average (MA) components to represent time series data, suitable for stationary series without the need for differencing." []
subset: aio:ModelSubset
synonym: "ARMA" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:AvailabilityHeuristicBias
name: Availability Heuristic Bias
def: "An individual bias characterized by a mental shortcut where easily recalled information is overweighted in judgment and decision-making." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Availability Bias" EXACT []
synonym: "Availability Heuristic" EXACT []
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:AverageLayer
name: Average Layer
def: "A merging layer that averages a list of inputs element-wise taking as input a list of tensors all of the same shape." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Average]
comment: Layer that averages a list of inputs element-wise. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
subset: aio:LayerSubset
is_a: aio:MergingLayer ! Merging Layer

[Term]
id: aio:AveragePooling1DLayer
name: AveragePooling1D Layer
def: "A pooling layer that performs average pooling for temporal data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling1D]
comment: Average pooling for temporal data. Downsamples the input representation by taking the average value over the window defined by pool_size. The window is shifted by strides. The resulting output when using "valid" padding option has a shape of: output_shape = (input_shape - pool_size + 1) / strides). The resulting output shape when using the "same" padding option is: output_shape = input_shape / strides.
subset: aio:LayerSubset
synonym: "AvgPool1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AveragePooling2DLayer
name: AveragePooling2D Layer
def: "A pooling layer that performs average pooling for spatial data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D]
comment: Average pooling operation for spatial data. Downsamples the input along its spatial dimensions (height and width) by taking the average value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension. The resulting output when using "valid" padding option has a shape (number of rows or columns) of: output_shape = math.floor((input_shape - pool_size) / strides) + 1 (when input_shape >= pool_size). The resulting output shape when using the "same" padding option is: output_shape = math.floor((input_shape - 1) / strides) + 1.
subset: aio:LayerSubset
synonym: "AvgPool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AveragePooling3DLayer
name: AveragePooling3D Layer
def: "A pooling layer that performs average pooling for 3D data (spatial or spatio-temporal)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling3D]
comment: Average pooling operation for 3D data (spatial or spatio-temporal). Downsamples the input along its spatial dimensions (depth, height, and width) by taking the average value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension.
subset: aio:LayerSubset
synonym: "AvgPool3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AvgPool1DLayer
name: AvgPool1D Layer
def: "A pooling layer that applies a 1D average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AvgPool1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AvgPool2DLayer
name: AvgPool2D Layer
def: "A pooling layer that applies a 2D average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AvgPool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:AvgPool3DLayer
name: AvgPool3D Layer
def: "A pooling layer that applies a 3D average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "AvgPool3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:BackfedInputLayer
name: Backfed Input Layer
def: "An input layer that receives values from another layer." []
subset: aio:LayerSubset
is_a: aio:InputLayer ! Input Layer

[Term]
id: aio:BatchNorm1DLayer
name: BatchNorm1D Layer
def: "A batch normalization layer that applies Batch Normalization over a 2D or 3D input." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
subset: aio:LayerSubset
synonym: "BatchNorm1D" EXACT []
is_a: aio:BatchNormalizationLayer ! BatchNormalization Layer

[Term]
id: aio:BatchNorm2DLayer
name: BatchNorm2D Layer
def: "A batch normalization layer that applies Batch Normalization over a 4D input." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
subset: aio:LayerSubset
synonym: "BatchNorm2D" EXACT []
is_a: aio:BatchNormalizationLayer ! BatchNormalization Layer

[Term]
id: aio:BatchNorm3DLayer
name: BatchNorm3D Layer
def: "A batch normalization layer that applies Batch Normalization over a 5D input." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
subset: aio:LayerSubset
synonym: "BatchNorm3D" EXACT []
is_a: aio:BatchNormalizationLayer ! BatchNormalization Layer

[Term]
id: aio:BatchNormalizationLayer
name: BatchNormalization Layer
def: "A normalization layer that normalizes its inputs applying a transformation that maintains the mean close to 0 and the standard deviation close to 1." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization]
comment: Layer that normalizes its inputs. Batch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1. Importantly, batch normalization works differently during training and during inference. During training (i.e. when using fit() or when calling the layer/model with the argument training=True), the layer normalizes its output using the mean and standard deviation of the current batch of inputs. That is to say, for each channel being normalized, the layer returns gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta, where: epsilon is small constant (configurable as part of the constructor arguments), gamma is a learned scaling factor (initialized as 1), which can be disabled by passing scale=False to the constructor. beta is a learned offset factor (initialized as 0), which can be disabled by passing center=False to the constructor. During inference (i.e. when using evaluate() or predict() or when calling the layer/model with the argument training=False (which is the default), the layer normalizes its output using a moving average of the mean and standard deviation of the batches it has seen during training. That is to say, it returns gamma * (batch - self.moving_mean) / sqrt(self.moving_var + epsilon) + beta. self.moving_mean and self.moving_var are non-trainable variables that are updated each time the layer in called in training mode, as such: moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum) moving_var = moving_var * momentum + var(batch) * (1 - momentum).
subset: aio:LayerSubset
synonym: "BatchNorm" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:BayesianNetwork
name: Bayesian Network
def: "A network that is a probabilistic graphical model representing variables and their conditional dependencies via a directed acyclic graph." [https://en.wikipedia.org/wiki/Bayesian_network]
subset: aio:NetworkSubset
is_a: aio:Network ! Network

[Term]
id: aio:BehavioralBias
name: Behavioral Bias
def: "An individual bias characterized by systematic distortions in user behavior across platforms or contexts or across users represented in different datasets." [https://doi.org/10.6028/NIST.SP.1270]
comment: Systematic distortions in user behavior across platforms or contexts, or across users represented in different datasets.
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:Bias
name: Bias
def: "A systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others." [https://www.merriam-webster.com/dictionary/bias]
subset: aio:BiasSubset

[Term]
id: aio:Biclustering
name: Biclustering
def: "A machine learning task focused on methods that simultaneously cluster the rows and columns of a matrix to identify submatrices with coherent patterns." [https://en.wikipedia.org/wiki/Biclustering]
subset: aio:MachineLearningSubset
synonym: "Block Clustering" EXACT []
synonym: "Co-clustering" EXACT []
synonym: "Joint Clustering" EXACT []
synonym: "Two-mode Clustering" EXACT []
synonym: "Two-way Clustering" EXACT []
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:BidirectionalLayer
name: Bidirectional Layer
def: "A recurrent layer that is a bidirectional wrapper for RNNs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Bidirectional]
comment: Bidirectional wrapper for RNNs.
subset: aio:LayerSubset
is_a: aio:RecurrentLayer ! Recurrent Layer

[Term]
id: aio:BidirectionalTransformerLanguageModel
name: Bidirectional Transformer Language Model
def: "A transformer language model such as BERT that uses the transformer architecture to build deep bidirectional representations by predicting masked tokens based on their context." [https://arxiv.org/abs/1810.04805, https://en.wikipedia.org/wiki/BERT_(language_model)]
subset: aio:ModelSubset
synonym: "BERT" EXACT []
synonym: "Bidirectional Transformer LM" EXACT []
is_a: aio:TransformerLanguageModel ! Transformer Language Model

[Term]
id: aio:BinaryClassification
name: Binary Classification
def: "A classification focused on methods that classify elements into two groups based on a classification rule." [https://en.wikipedia.org/wiki/Binary_classification]
subset: aio:MachineLearningSubset
is_a: aio:Classification ! Classification

[Term]
id: aio:BoltzmannMachineNetwork
name: Boltzmann Machine Network
def: "A symmetrically connected network that is a type of stochastic recurrent neural network and Markov random field." [https://en.wikipedia.org/wiki/Boltzmann_machine]
comment: Layers: Backfed Input, Probabilistic Hidden
subset: aio:NetworkSubset
synonym: "BM" EXACT []
synonym: "Sherrington–Kirkpatrick model with external field" EXACT []
synonym: "stochastic Hopfield network with hidden units" EXACT []
synonym: "stochastic Ising-Lenz-Little model" EXACT []
is_a: aio:SymmetricallyConnectedNetwork ! Symmetrically Connected Network
relationship: BFO:0000051 aio:BackfedInputLayer ! has part Backfed Input Layer
relationship: BFO:0000051 aio:ProbabilisticHiddenLayer ! has part Probabilistic Hidden Layer

[Term]
id: aio:CategoricalFeaturesPreprocessingLayer
name: Categorical Features Preprocessing Layer
def: "A layer that performs categorical data preprocessing operations." [https://keras.io/guides/preprocessing_layers/]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:CategoryEncodingLayer
name: CategoryEncoding Layer
def: "A categorical features preprocessing layer that encodes integer features providing options for condensing data into a categorical encoding." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/CategoryEncoding]
comment: A preprocessing layer which encodes integer features. This layer provides options for condensing data into a categorical encoding when the total number of tokens are known in advance. It accepts integer values as inputs, and it outputs a dense or sparse representation of those inputs. For integer inputs where the total number of tokens is not known, use tf.keras.layers.IntegerLookup instead.
subset: aio:LayerSubset
is_a: aio:CategoricalFeaturesPreprocessingLayer ! Categorical Features Preprocessing Layer

[Term]
id: aio:CausalGraphicalModel
name: Causal Graphical Model
def: "A probabilistic graphical model used to encode assumptions about the data-generating process." [https://en.wikipedia.org/wiki/Causal_graph]
subset: aio:MachineLearningSubset
synonym: "Casaul Bayesian Network" EXACT []
synonym: "Casaul Graph" EXACT []
synonym: "DAG" EXACT []
synonym: "Directed Acyclic Graph" EXACT []
synonym: "Path Diagram" EXACT []
is_a: aio:ProbabilisticGraphicalModel ! Probabilistic Graphical Model

[Term]
id: aio:CausalLLM
name: Causal LLM
def: "A large language model that only attends to previous tokens in the sequence when generating text modeling the probability distribution autoregressively from left-to-right or causally." []
subset: aio:ModelSubset
synonym: "autoregressive" RELATED []
synonym: "Causal Large Language Model" EXACT []
synonym: "unidirectional" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:CenterCropLayer
name: CenterCrop Layer
def: "An image preprocessing layer that crops the central portion of images to a target size." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/CenterCrop]
comment: A preprocessing layer which crops images. This layers crops the central portion of the images to a target size. If an image is smaller than the target size, it will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:Classification
name: Classification
def: "A supervised learning focused on methods that distinguish and distribute kinds of \"things\" into different groups." [https://en.wikipedia.org/wiki/Classification_(general_theory)]
comment: Methods that distinguish and distribute kinds of "things" into different groups.
subset: aio:MachineLearningSubset
is_a: aio:SupervisedLearning ! Supervised Learning

[Term]
id: aio:Cleaning
name: Cleaning
def: "A data preparation that removes noise inconsistencies and irrelevant information from data to enhance its quality and prepare it for analysis or further processing." []
subset: aio:PreprocessingSubset
synonym: "Data cleaning" RELATED []
synonym: "Data Cleansing" EXACT []
synonym: "Standardization" EXACT []
synonym: "Text normalization" RELATED []
is_a: aio:DataPreparation ! Data Preparation

[Term]
id: aio:Clustering
name: Clustering
def: "A machine learning task focused on methods that group a set of objects such that objects in the same group are more similar to each other than to those in other groups." [https://en.wikipedia.org/wiki/Cluster_analysis]
subset: aio:MachineLearningSubset
synonym: "Cluster analysis" EXACT []
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:CognitiveBias
name: Cognitive Bias
def: "An individual bias characterized by deviations from rational judgment and decision-making including adaptive mental shortcuts known as heuristics." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:CompositionalGeneralizationLLM
name: Compositional Generalization LLM
def: "A large language model that is trained to understand and recombine the underlying compositional structures in language enabling better generalization to novel combinations and out-of-distribution examples." []
subset: aio:ModelSubset
synonym: "Compositional Generalization Large Language Model" EXACT []
synonym: "out-of-distribution generalization" RELATED []
synonym: "systematic generalization" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ComputationalBias
name: Computational Bias
def: "A bias caused by differences between results and facts in the process of data analysis (including the source of data the estimator chose) and analysis methods." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Statistical Bias" EXACT []
is_a: aio:Bias ! Bias

[Term]
id: aio:ConcatenateLayer
name: Concatenate Layer
def: "A merging layer that concatenates a list of inputs taking as input a list of tensors all of the same shape except for the concatenation axis." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Concatenate]
comment: Layer that concatenates a list of inputs. It takes as input a list of tensors, all of the same shape except for the concatenation axis, and returns a single tensor that is the concatenation of all inputs.
subset: aio:LayerSubset
is_a: aio:MergingLayer ! Merging Layer

[Term]
id: aio:ConceptDriftBias
name: Concept Drift Bias
def: "A use and interpretation bias due to the use of a system outside its planned domain of application causing performance gaps between laboratory settings and the real world." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Concept Drift" EXACT []
is_a: aio:UseAndInterpretationBias ! Use And Interpretation Bias

[Term]
id: aio:ConfirmationBias
name: Confirmation Bias
def: "An individual bias characterized by the tendency to prefer information that confirms existing beliefs influencing the search for interpretation of and recall of information." [https://doi.org/10.6028/NIST.SP.1270]
comment: The tendency to prefer information that confirms existing beliefs, influencing the search for, interpretation of, and recall of information.
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:ConsumerBias
name: Consumer Bias
def: "An individual bias arising when an algorithm or platform provides users a venue to express their biases occurring from either side in a digital interaction." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:ContentProductionBias
name: Content Production Bias
def: "A use and interpretation bias arising from structural lexical semantic and syntactic differences in user-generated content." [https://doi.org/10.6028/NIST.SP.1270]
comment: Bias from structural, lexical, semantic, and syntactic differences in user-generated content.
subset: aio:BiasSubset
is_a: aio:UseAndInterpretationBias ! Use And Interpretation Bias

[Term]
id: aio:ContinualLearning
name: Continual Learning
def: "A deep neural network that learns sequential tasks without forgetting knowledge from preceding tasks and without access to old task data during new task training." [https://paperswithcode.com/task/continual-learning]
comment: Learning a model for sequential tasks without forgetting knowledge from preceding tasks, with no access to old task data during new task training.
subset: aio:NetworkSubset
synonym: "Incremental Learning" EXACT []
synonym: "Life-Long Learning" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:ContinualLearningLLM
name: Continual Learning LLM
def: "A large language model that continually acquires new knowledge and skills over time without forgetting previously learned information allowing the model to adapt and expand its capabilities as new data becomes available." []
subset: aio:ModelSubset
synonym: "catastrophic forgetting" RELATED []
synonym: "CL-Large Language Model" EXACT []
synonym: "Continual Learning Large Language Model" EXACT []
synonym: "lifelong learning" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ContrastiveLearning
name: Contrastive Learning
def: "A deep neural network self-supervised learning approach that learns to distinguish between similar and dissimilar data samples." [https://arxiv.org/abs/2202.14037]
comment: Contrastive learning is a self-supervised learning approach in which the model learns to distinguish between similar and dissimilar pairs of data samples. By maximizing the similarity between positive pairs (similar samples) and minimizing the similarity between negative pairs (dissimilar samples), the model learns to capture meaningful representations of the data. This method is particularly effective for representation learning and is widely used in tasks such as image classification, clustering, and retrieval. Contrastive learning techniques often employ loss functions such as the contrastive loss or the triplet loss to achieve these objectives.
subset: aio:NetworkSubset
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:ContrastiveLearningLLM
name: Contrastive Learning LLM
def: "A large language model that is trained to pull semantically similar samples closer together and push dissimilar samples apart in the representation space learning high-quality features useful for downstream tasks." []
subset: aio:ModelSubset
synonym: "Representation learning" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ControllableLLM
name: Controllable LLM
def: "A large language model that allows for explicit control over certain attributes of the generated text such as style tone topic or other desired characteristics through conditioning or specialized training objectives." []
comment: A controllable LLM allows for explicit control over certain attributes of the generated text, such as style, tone, topic, or other desired characteristics, through conditioning or specialized training objectives.
subset: aio:ModelSubset
synonym: "conditional generation" RELATED []
synonym: "Controllable Large Language Model" EXACT []
synonym: "guided generation" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ConvLSTM1DLayer
name: ConvLSTM1D Layer
def: "A convolutional layer that implements a 1D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ConvLSTM1D]
comment: 1D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
subset: aio:LayerSubset
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:ConvLSTM2DLayer
name: ConvLSTM2D Layer
def: "A convolutional layer that implements a 2D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ConvLSTM2D]
comment: 2D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
subset: aio:LayerSubset
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:ConvLSTM3DLayer
name: ConvLSTM3D Layer
def: "A convolutional layer that implements a 3D Convolutional LSTM similar to an LSTM but with convolutional input and recurrent transformations." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ConvLSTM3D]
comment: 3D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
subset: aio:LayerSubset
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:Convolution1DLayer
name: Convolution1D Layer
def: "A convolutional layer that implements 1D convolution (e.g. temporal convolution)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D]
subset: aio:LayerSubset
synonym: "Conv1D" EXACT []
synonym: "Conv1D Layer" EXACT []
synonym: "Convolution1D" EXACT []
synonym: "nn.Conv1D" EXACT []
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:Convolution1DTransposeLayer
name: Convolution1DTranspose Layer
def: "A convolutional layer that implements transposed 1D convolution sometimes called deconvolution." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1DTranspose]
comment: Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 3) for data with 128 time steps and 3 channels.
subset: aio:LayerSubset
synonym: "Conv1DTranspose Layer" EXACT []
synonym: "Convolution1DTranspose" EXACT []
synonym: "ConvTranspose1D" EXACT []
synonym: "nn.ConvTranspose1D" EXACT []
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:Convolution2DLayer
name: Convolution2D Layer
def: "A convolutional layer that implements 2D convolution (e.g. spatial convolution over images)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D]
comment: 2D convolution layer (e.g. spatial convolution over images). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last". You can use None when a dimension has variable size.
subset: aio:LayerSubset
synonym: "Conv2D" EXACT []
synonym: "Conv2D Layer" EXACT []
synonym: "Convolution2D" EXACT []
synonym: "nn.Conv2D" EXACT []
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:Convolution2DTransposeLayer
name: Convolution2DTranspose Layer
def: "A convolutional layer that implements transposed 2D convolution" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2DTranspose]
comment: Transposed convolution layer (sometimes called Deconvolution).
subset: aio:LayerSubset
synonym: "Conv2DTranspose Layer" EXACT []
synonym: "Convolution2DTranspose" EXACT []
synonym: "ConvTranspose2D" EXACT []
synonym: "nn.ConvTranspose2D" EXACT []
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:Convolution3DLayer
name: Convolution3D Layer
def: "A convolutional layer that implements 3D convolution (e.g. spatial convolution over volumes)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv3D]
comment: 3D convolution layer (e.g. spatial convolution over volumes). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 128, 1) for 128x128x128 volumes with a single channel, in data_format="channels_last".
subset: aio:LayerSubset
synonym: "Conv3D" EXACT []
synonym: "Conv3D Layer" EXACT []
synonym: "Convolution3D" EXACT []
synonym: "nn.Conv3D" EXACT []
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:Convolution3DTransposeLayer
name: Convolution3DTranspose Layer
def: "A convolutional layer that implements transposed 3D convolution" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv3DTranspose]
comment: Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 128, 3) for a 128x128x128 volume with 3 channels if data_format="channels_last".
subset: aio:LayerSubset
synonym: "Conv3DTranspose Layer" EXACT []
synonym: "Convolution3DTranspose" EXACT []
synonym: "ConvTranspose3D" EXACT []
synonym: "nn.ConvTranspose3D" EXACT []
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:ConvolutionalLayer
name: Convolutional Layer
def: "A layer that contains a set of filters (or kernels) parameters of which are to be learned throughout the training." [https://www.sciencedirect.com/topics/engineering/convolutional-layer]
comment: A convolutional layer is the main building block of a CNN. It contains a set of filters (or kernels), parameters of which are to be learned throughout the training. The size of the filters is usually smaller than the actual image. Each filter convolves with the image and creates an activation map.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:Cropping1DLayer
name: Cropping1D Layer
def: "A reshaping layer that crops along the time dimension (axis 1) for 1D input." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Cropping1D]
comment: Cropping layer for 1D input (e.g. temporal sequence). It crops along the time dimension (axis 1).
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:Cropping2DLayer
name: Cropping2D Layer
def: "A layer that crops along spatial dimensions (i.e. height and width) for 2D input." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Cropping2D]
comment: Cropping layer for 2D input (e.g. picture). It crops along spatial dimensions, i.e. height and width.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:Cropping3DLayer
name: Cropping3D Layer
def: "A layer that crops along spatial dimensions (depth" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Cropping3D]
comment: Cropping layer for 3D data (e.g. spatial or spatio-temporal).
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:CrossDomainLLM
name: Cross-Domain LLM
def: "A large language model that performs well across a wide range of domains without significant loss in performance, facilitated by advanced domain adaptation techniques." []
subset: aio:ModelSubset
synonym: "cross-domain transfer" RELATED []
synonym: "domain adaptation" RELATED []
synonym: "Domain-General LLM" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:CurriculumLearning
name: Curriculum Learning
def: "A training strategy in machine learning where models are trained on data in a meaningful order starting with simpler examples and gradually increasing the complexity to improve learning efficiency and model performance." []
subset: aio:TrainingStrategySubset
synonym: "Complexity grading" RELATED []
synonym: "Sequential Learning" EXACT []
synonym: "Sequential learning" RELATED []
synonym: "Structured Learning" EXACT []
is_a: aio:TrainingStrategy ! TrainingStrategy

[Term]
id: aio:CurriculumLearningLLM
name: Curriculum Learning LLM
def: "A large language model that is trained by presenting learning examples in a meaningful order from simple to complex mimicking the learning trajectory followed by humans." []
subset: aio:ModelSubset
synonym: "Learning progression" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:DataAugmentation
name: Data Augmentation
def: "A data enhancement used to increase the diversity and quantity of training data by applying various transformations such as rotation scaling flipping and cropping to existing data samples enhancing the robustness and performance of machine learning models." []
subset: aio:PreprocessingSubset
synonym: "Data Enrichment" EXACT []
synonym: "Data Expansion" EXACT []
synonym: "Paraphrasing" RELATED []
synonym: "Synonym replacement" RELATED []
is_a: aio:DataEnhancement ! DataEnhancement

[Term]
id: aio:DataDredgingBias
name: Data Dredging Bias
def: "A use and interpretation bias where testing many hypotheses in a dataset may yield apparent statistical significance even when results are nonsignificant." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Data Dredging" EXACT []
is_a: aio:UseAndInterpretationBias ! Use And Interpretation Bias

[Term]
id: aio:DataEnhancement
name: DataEnhancement
def: "A preprocessing used to improve the quality diversity and volume of data available for training machine learning models, such as data augmentation synthesis and enrichment to enhance model robustness and accuracy." []
subset: aio:PreprocessingSubset
synonym: "AIO:PreprocessingSubset" EXACT []
is_a: aio:Preprocessing ! Preprocessing

[Term]
id: aio:DataGenerationBias
name: Data Generation Bias
def: "A selection and sampling bias arising from adding synthetic or redundant data samples to a dataset." [https://en.wikipedia.org/wiki/Selection_bias]
comment: Bias from adding synthetic or redundant data samples to a dataset.
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:DataImputation
name: Data Imputation
def: "A machine learning task focused on methods that replace missing data with substituted values." [https://en.wikipedia.org/wiki/Imputation_(statistics)]
comment: Methods that replace missing data with substituted values.
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:DataPreparation
name: Data Preparation
def: "A preprocessing that cleans, transforms and organizes raw data into a suitable format for analysis and modeling, ensuring the quality and relevance of the data for machine learning tasks." []
subset: aio:PreprocessingSubset
synonym: "Data Assembly" EXACT []
synonym: "Data Curation" EXACT []
synonym: "Data Processing" EXACT []
is_a: aio:Preprocessing ! Preprocessing

[Term]
id: aio:DatatoTextLLM
name: Data-to-Text LLM
def: "A large language model that generates natural language descriptions from structured data sources like tables, graphs, and knowledge bases, requiring grounding in meaning representations." []
subset: aio:ModelSubset
synonym: "Meaning representation" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:DecisionTree
name: Decision Tree
def: "A classification that uses a tree-like model of decisions and their possible consequences including chance event outcomes resource costs and utilities." [https://en.wikipedia.org/wiki/Decision_tree]
comment: A decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utilities.
subset: aio:MachineLearningSubset
is_a: aio:Classification ! Classification

[Term]
id: aio:DecoderLLM
name: Decoder LLM
def: "A large language model that uses a decoder-only architecture consisting of only a decoder trained to predict the next token in a sequence given the previous tokens." [https://www.practicalai.io/understanding-transformer-model-architectures/#\:~\:text=Encoder]
comment: A decoder-only architecture consisting of only a decoder, trained to predict the next token in a sequence given the previous tokens. Unlike the encoder-decoder architecture, it does not have an explicit encoder and encodes information implicitly in the hidden state of the decoder, updated at each step of the generation process.
subset: aio:ModelSubset
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:DeconvolutionalNetwork
name: Deconvolutional Network
def: "A deep neural network that uses deconvolution for unsupervised construction of hierarchical image representations." [https://ieeexplore.ieee.org/document/5539957]
comment: Layers: Input, Kernel, Convolutional/Pool, Output
subset: aio:NetworkSubset
synonym: "DN" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network
relationship: BFO:0000051 aio:ConvolutionalLayer ! has part Convolutional Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:KernelLayer ! has part Kernel Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer
relationship: BFO:0000051 aio:PoolingLayer ! has part Pooling Layer

[Term]
id: aio:DeepActiveLearning
name: Deep Active Learning
def: "A deep neural network that combines deep learning and active learning to maximize model performance while annotating the fewest samples possible." [https://arxiv.org/pdf/2009.00236.pdf]
subset: aio:NetworkSubset
synonym: "DeepAL" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:DeepBeliefNetwork
name: Deep Belief Network
def: "An unsupervised pretrained network composed of multiple layers of latent variables that learns to probabilistically reconstruct inputs and perform classification." [https://en.wikipedia.org/wiki/Deep_belief_network]
comment: Layers: Backfed Input, Probabilistic Hidden, Hidden, Matched Output-Input
subset: aio:NetworkSubset
synonym: "DBN" EXACT []
is_a: aio:UnsupervisedPretrainedNetwork ! Unsupervised Pretrained Network
relationship: BFO:0000051 aio:BackfedInputLayer ! has part Backfed Input Layer
relationship: BFO:0000051 aio:MatchedInputOutputLayer ! has part Matched Input-Output Layer
relationship: BFO:0000051 aio:ProbabilisticHiddenLayer ! has part Probabilistic Hidden Layer

[Term]
id: aio:DeepConvolutionalInverseGraphicsNetwork
name: Deep Convolutional Inverse Graphics Network
def: "An autoencoder network that learns interpretable disentangled image representations through convolution and de-convolution layers trained with the stochastic gradient variational Bayes algorithm." []
comment: Layers: Input, Kernel, Convolutional/Pool, Probabilistic Hidden, Convolutional/Pool, Kernel, Output
subset: aio:NetworkSubset
synonym: "DCIGN" EXACT []
is_a: aio:AutoEncoderNetwork ! Auto Encoder Network
relationship: BFO:0000051 aio:ConvolutionalLayer ! has part Convolutional Layer
relationship: BFO:0000051 aio:KernelLayer ! has part Kernel Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer
relationship: BFO:0000051 aio:PoolingLayer ! has part Pooling Layer
relationship: BFO:0000051 aio:ProbabilisticHiddenLayer ! has part Probabilistic Hidden Layer

[Term]
id: aio:DeepConvolutionalNetwork
name: Deep Convolutional Network
def: "A deep neural network specialized for analyzing visual imagery using shared-weight architecture and translation-equivariant feature maps." [https://en.wikipedia.org/wiki/Convolutional_neural_network]
comment: Layers: Input, Kernel, Convolutional/Pool, Hidden, Output
subset: aio:NetworkSubset
synonym: "CNN" EXACT []
synonym: "ConvNet" EXACT []
synonym: "Convolutional Neural Network" EXACT []
synonym: "DCN" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network
relationship: BFO:0000051 aio:ConvolutionalLayer ! has part Convolutional Layer
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:KernelLayer ! has part Kernel Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer
relationship: BFO:0000051 aio:PoolingLayer ! has part Pooling Layer

[Term]
id: aio:DeepFeedForwardNetwork
name: Deep Feed-Forward Network
def: "A deep neural network that processes information in one direction—from input nodes through hidden nodes to output nodes—without cycles or loops." [https://en.wikipedia.org/wiki/Feedforward_neural_network]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "DFF" EXACT []
synonym: "MLP" EXACT []
synonym: "Multilayer Perceptoron" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:DeepNeuralNetwork
name: Deep Neural Network
def: "An artificial neural network characterized by multiple hidden layers between the input and output layers." []
comment: A deep neural network (DNN) is a type of artificial neural network (ANN) characterized by multiple hidden layers between the input and output layers. Each layer consists of interconnected neurons that process and transmit information. DNNs can model complex patterns and representations in data through their hierarchical structure, where each layer extracts increasingly abstract features from the input. DNNs are widely used in various applications, including image and speech recognition, natural language processing, and more, due to their ability to learn and generalize from large amounts of data.
subset: aio:NetworkSubset
synonym: "DNN" EXACT []
is_a: aio:ArtificialNeuralNetwork ! Artificial Neural Network

[Term]
id: aio:DeepTransferLearning
name: Deep Transfer Learning
def: "A deep neural network that relaxes the hypothesis that training data must be independent and identically distributed with test data to address insufficient training data." [https://arxiv.org/abs/1808.01974]
subset: aio:NetworkSubset
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:DenoisingAutoEncoder
name: Denoising Auto Encoder
def: "An autoencoder network trained to reconstruct the original undistorted input from a partially corrupted input." [https://doi.org/10.1145/1390156.1390294]
comment: Layers: Noisy Input, Hidden, Matched Output-Input
subset: aio:NetworkSubset
synonym: "DAE" EXACT []
synonym: "Denoising Autoencoder" EXACT []
is_a: aio:AutoEncoderNetwork ! Auto Encoder Network
relationship: BFO:0000051 aio:NoisyInputLayer ! has part Noisy Input Layer

[Term]
id: aio:DenseFeaturesLayer
name: DenseFeatures Layer
def: "A layer that produces a dense tensor based on given feature columns." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/DenseFeatures]
comment: A layer that produces a dense Tensor based on given feature_columns. Generally a single example in training data is described with FeatureColumns. At the first layer of the model, this column oriented data should be converted to a single Tensor. This layer can be called multiple times with different features. This is the V2 version of this layer that uses name_scopes to create variables instead of variable_scopes. But this approach currently lacks support for partitioned variables. In that case, use the V1 version instead.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:DenseLayer
name: Dense Layer
def: "A layer that is a regular densely-connected neural network layer." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense]
comment: Just your regular densely-connected NN layer.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:DeploymentBias
name: Deployment Bias
def: "A group bias arising when systems are used as decision aids for humans since the human intermediary may act on predictions in ways that are typically not modeled in the system." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:GroupBias ! Group Bias

[Term]
id: aio:DepthwiseConv1DLayer
name: DepthwiseConv1D Layer
def: "A convolutional layer that performs depthwise 1D convolution" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/DepthwiseConv1D]
comment: Depthwise 1D convolution. Depthwise convolution is a type of convolution in which each input channel is convolved with a different kernel (called a depthwise kernel). You can understand depthwise convolution as the first step in a depthwise separable convolution. It is implemented via the following steps: Split the input into individual channels. Convolve each channel with an individual depthwise kernel with depth_multiplier output channels. Concatenate the convolved outputs along the channels axis. Unlike a regular 1D convolution, depthwise convolution does not mix information across different input channels. The depth_multiplier argument determines how many filter are applied to one input channel. As such, it controls the amount of output channels that are generated per input channel in the depthwise step.
subset: aio:LayerSubset
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:DepthwiseConv2DLayer
name: DepthwiseConv2D Layer
def: "A convolutional layer that performs depthwise 2D convolution" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/DepthwiseConv2D]
comment: Depthwise 2D convolution.
subset: aio:LayerSubset
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:DetectionBias
name: Detection Bias
def: "A selection and sampling bias characterized by systematic differences between groups in how outcomes are determined potentially over- or underestimating effect size." [https://doi.org/10.6028/NIST.SP.1270]
comment: Systematic differences between groups in how outcomes are determined, potentially over- or underestimating effect size.
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:DialogueLLM
name: Dialogue LLM
def: "A large language model that is optimized for engaging in multi-turn conversations understanding context and generating relevant coherent responses continuously over many dialogue turns." []
subset: aio:ModelSubset
synonym: "conversational AI" RELATED []
synonym: "Dialogue Large Language Model" EXACT []
synonym: "multi-turn dialogue" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:DifferentiableLLM
name: Differentiable LLM
def: "A large language model that has an architecture amenable to full end-to-end training via backpropagation without relying on teacher forcing or unlikelihood training objectives." []
subset: aio:ModelSubset
synonym: "Differentiable Large Language Model" EXACT []
synonym: "end-to-end training" RELATED []
synonym: "fully backpropagable" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:DimensionalityReduction
name: Dimensionality Reduction
def: "An unsupervised learning focused on the process of transforming data from a high-dimensional space into a lower-dimensional space while retaining meaningful properties of the original data." [https://en.wikipedia.org/wiki/Dimensionality_reduction]
subset: aio:MachineLearningSubset
synonym: "Dimension Reduction" EXACT []
is_a: aio:UnsupervisedLearning ! Unsupervised Learning

[Term]
id: aio:DiscretizationLayer
name: Discretization Layer
def: "A numerical features prepreprocessing layer which buckets continuous features by ranges." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Discretization]
subset: aio:LayerSubset
is_a: aio:NumericalFeaturesPreprocessingLayer ! Numerical Features Preprocessing Layer

[Term]
id: aio:Distillation
name: Distillation
def: "A preprocessing that trains a smaller model to replicate the behavior of a larger model aiming to compress the knowledge into a more compact form without significant loss of performance." [https://doi.org/10.48550/arXiv.2105.13093]
subset: aio:PreprocessingSubset
synonym: "Knowledge compression" RELATED []
synonym: "Purification" EXACT []
synonym: "Refining" EXACT []
synonym: "Teacher-student model" RELATED []
is_a: aio:TrainingStrategy ! TrainingStrategy

[Term]
id: aio:DomainAdaptedLLM
name: Domain-Adapted LLM
def: "A large language model which is pre-trained on a broad corpus and then fine-tuned on domain-specific data to specialize its capabilities for particular domains or applications, like scientific literature or code generation." []
subset: aio:ModelSubset
synonym: "domain robustness" RELATED []
synonym: "Domain-Adapted Large Language Model" EXACT []
synonym: "transfer learning" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:DotLayer
name: Dot Layer
def: "A layer that computes a dot product between samples in two tensors." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dot]
comment: Layer that computes a dot product between samples in two tensors. E.g. if applied to a list of two tensors a and b of shape (batch_size, n), the output will be a tensor of shape (batch_size, 1) where each entry i will be the dot product between a[i] and b[i].
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:DropoutLayer
name: Dropout Layer
def: "A regularization layer that applies Dropout to the input" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout]
comment: Applies Dropout to the input. The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by 1/(1 - rate) such that the sum over all inputs is unchanged. Note that the Dropout layer only applies when training is set to True such that no values are dropped during inference. When using model.fit, training will be appropriately set to True automatically, and in other contexts, you can set the kwarg explicitly to True when calling the layer. (This is in contrast to setting trainable=False for a Dropout layer. trainable does not affect the layer's behavior, as Dropout does not have any variables/weights that can be frozen during training.)
subset: aio:LayerSubset
is_a: aio:RegularizationLayer ! Regularization Layer

[Term]
id: aio:DunningKrugerEffectBias
name: Dunning-Kruger Effect Bias
def: "A cognitive bias in which people with low ability in an area overestimate that ability. Often measured by comparing self-assessment with objective performance." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Dunning-Kruger Effect" EXACT []
is_a: aio:CognitiveBias ! Cognitive Bias

[Term]
id: aio:DynamicConditionalCorrelation
name: Dynamic Conditional Correlation
def: "A model that allows for time-varying correlations between different time series, used in financial econometrics to model and forecast covariances." []
subset: aio:ModelSubset
synonym: "DCC" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:ELUFunction
name: ELU Function
def: "A mathematical function that is x if x > 0 and alpha * (exp(x) - 1) if x < 0 where alpha controls the value to which an ELU saturates for negative net inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/elu]
comment: The exponential linear unit (ELU) with alpha > 0 is: x if x > 0 and alpha * (exp(x) - 1) if x < 0 The ELU hyperparameter alpha controls the value to which an ELU saturates for negative net inputs. ELUs diminish the vanishing gradient effect. ELUs have negative values which pushes the mean of the activations closer to zero. Mean activations that are closer to zero enable faster Learning as they bring the gradient closer to the natural gradient. ELUs saturate to a negative value when the argument gets smaller. Saturation means a small derivative which decreases the variation and the information that is propagated to the next layer.
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
synonym: "ELU" EXACT []
synonym: "Exponential Linear Unit" EXACT []
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:ELULayer
name: ELU Layer
def: "An activation layer that applies the Exponential Linear Unit (ELU) function element-wise." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ELU]
comment: Exponential Linear Unit.
subset: aio:LayerSubset
is_a: aio:ActivationLayer ! Activation Layer

[Term]
id: aio:EchoStateNetwork
name: Echo State Network
def: "A recurrent neural network with a recurrent hidden layer and sparsely connected hidden neurons that learns output weights to produce temporal patterns." [https://en.wikipedia.org/wiki/Echo_state_network]
comment: Layers: Input, Recurrent, Output
subset: aio:NetworkSubset
synonym: "ESN" EXACT []
is_a: aio:RecurrentNeuralNetwork ! Recurrent Neural Network
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer
relationship: BFO:0000051 aio:RecurrentLayer ! has part Recurrent Layer

[Term]
id: aio:EcologicalFallacyBias
name: Ecological Fallacy Bias
def: "A selection and sampling bias occurring when an inference about an individual is made based on their group membership." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Ecological Fallacy" EXACT []
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:EmbeddingLayer
name: Embedding Layer
def: "A layer that turns positive integers (indexes) into dense vectors of fixed size." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:EmbodiedLLM
name: Embodied LLM
def: "A large language model that integrates language with other modalities like vision audio and robotics to enable grounded language understanding in real-world environments." []
comment: An embodied LLM integrates language with other modalities like vision, audio, and robotics to enable grounded language understanding in real-world environments.
subset: aio:ModelSubset
synonym: "Embodied Large Language Model" EXACT []
synonym: "multimodal grounding" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:EmergentBias
name: Emergent Bias
def: "A use and interpretation bias resulting from the use and reliance on algorithms across new or unanticipated contexts." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:UseAndInterpretationBias ! Use And Interpretation Bias

[Term]
id: aio:EncoderDecoderLLM
name: Encoder-Decoder LLM
def: "A large language model introduced in the \"Attention Is All You Need\" paper. The encoder processes the input sequence to generate a hidden representation summarizing the input information, while the decoder uses this hidden representation to generate the desired output sequence." [https://www.practicalai.io/understanding-transformer-model-architectures/#\:~\:text=Encoder]
subset: aio:ModelSubset
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:EncoderLLM
name: Encoder LLM
def: "A large language model that uses an encoder-only architecture to encode the input sequence into a fixed-length representation which is then used as input to a classifier or regressor for prediction." [https://www.practicalai.io/understanding-transformer-model-architectures/#\:~\:text=Encoder]
comment: An encoder-only architecture that encodes the input sequence into a fixed-length representation, which is then used as input to a classifier or regressor for prediction. The model has a pre-trained general-purpose encoder that requires fine-tuning for specific tasks.
subset: aio:ModelSubset
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:EnergyBasedLLM
name: Energy-Based LLM
def: "A large language model which models the explicit probability density over token sequences using an energy function, rather than an autoregressive factorization. This can improve modeling of long-range dependencies and global coherence." []
subset: aio:ModelSubset
synonym: "energy scoring" RELATED []
synonym: "Energy-Based Large Language Model" EXACT []
synonym: "explicit density modeling" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:EnsembleLearning
name: Ensemble Learning
def: "A machine learning task focused on methods that use multiple learning algorithms to achieve better predictive performance than any of the constituent algorithms alone." [https://en.wikipedia.org/wiki/Ensemble_learning]
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:ErrorPropagationBias
name: Error Propagation Bias
def: "A processing bias characterized by the effect of variables' uncertainties (or errors more specifically random errors) on the uncertainty of a function based on them." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Error Propagation" EXACT []
is_a: aio:ProcessingBias ! Processing Bias

[Term]
id: aio:EthicalLLM
name: Ethical LLM
def: "A large language model that is trained to uphold certain ethical principles values or rules in its language generation to increase safety and trustworthiness." []
subset: aio:ModelSubset
synonym: "constituitional AI" RELATED []
synonym: "Ethical Large Language Model" EXACT []
synonym: "value alignment" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:EvaluationBias
name: Evaluation Bias
def: "A selection and sampling bias arising when testing populations do not equally represent user populations or when inappropriate performance metrics are used." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:EvolutionaryLLM
name: Evolutionary LLM
def: "A large language model that applies principles of evolutionary computation to optimize its structure and parameters evolving over time to improve performance." []
subset: aio:ModelSubset
synonym: "evolutionary algorithms" RELATED []
synonym: "Evolutionary Language Model" EXACT []
synonym: "genetic programming" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ExclusionBias
name: Exclusion Bias
def: "A selection and sampling bias occurring when specific groups of user populations are excluded from testing and analysis." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:ExplainableLLM
name: Explainable LLM
def: "A large language model that is designed to provide insights into its decision-making process making it easier for users to understand and trust the model's outputs by incorporating mechanisms for interpreting and explaining its predictions in human-understandable terms." []
subset: aio:ModelSubset
synonym: "Explainable Language Model" EXACT []
synonym: "interpretability" RELATED []
synonym: "model understanding" RELATED []
synonym: "XAI LLM" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ExponentialFunction
name: Exponential Function
def: "A mathematical function denoted by f(x)=exp or e^{x}." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/exponential]
comment: The exponential function is a mathematical function denoted by f(x)=exp or e^\{x\}.
subset: aio:FunctionSubset
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:ExponentialSmoothingStateSpaceModel
name: Exponential Smoothing State Space Model
def: "A model that combines exponential smoothing with state space modeling, allowing for the inclusion of both trend and seasonal components. Used in  forecasting." []
subset: aio:ModelSubset
synonym: "ETS" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:ExtremeLearningMachine
name: Extreme Learning Machine
def: "A feedback network with randomly assigned hidden nodes that are not updated during training." [https://en.wikipedia.org/wiki/Extreme_Learning_machine]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "ELM" EXACT []
is_a: aio:FeedbackNetwork ! Feedback Network

[Term]
id: aio:FactoredLanguageModel
name: Factored Language Model
def: "A language model that views each word as a vector of multiple factors such as part-of-speech morphology and semantics to improve language modeling." [https://en.wikipedia.org/wiki/Factored_language_model]
subset: aio:ModelSubset
synonym: "Factorized Language Model" EXACT []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:FactorizedLLM
name: Factorized LLM
def: "A large language model that decomposes the full language modeling task into multiple sub-components or experts that each focus on a subset of the information enabling more efficient scaling." [https://doi.org/10.48550/arXiv.2403.12556]
subset: aio:ModelSubset
synonym: "Conditional masking" RELATED []
synonym: "Factorized Large Language Model" EXACT []
synonym: "Factorized Learning Assisted with Large Language Model" EXACT []
synonym: "Product of experts" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:FeatureExtraction
name: Feature Extraction
def: "A data enhancement that transforms raw data into a set of measurable characteristics that can be used as input for machine learning algorithms, enhancing the ability to make accurate predictions." []
subset: aio:PreprocessingSubset
synonym: "Attribute Extraction" EXACT []
synonym: "Feature Isolation" EXACT []
synonym: "Semantic embeddings" RELATED []
synonym: "Syntactic information" RELATED []
is_a: aio:DataEnhancement ! DataEnhancement

[Term]
id: aio:FederatedLLM
name: Federated LLM
def: "A large language model that is trained in a decentralized manner across multiple devices or silos without directly sharing private data enabling collaborative training while preserving data privacy and security." []
subset: aio:ModelSubset
synonym: "decentralized training" RELATED []
synonym: "Federated Large Language Model" EXACT []
synonym: "privacy-preserving" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:FederatedLearning
name: Federated Learning
def: "A deep neural network trained across decentralized edge devices or servers holding local data samples without exchanging them." [https://en.wikipedia.org/wiki/Federated_learning]
comment: Training an algorithm across multiple decentralized edge devices or servers holding local data samples without exchanging them.
subset: aio:NetworkSubset
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:FeedbackLoopBias
name: Feedback Loop Bias
def: "A use and interpretation bias occurring when an algorithm learns from user behavior and feeds that behavior back into the model." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:UseAndInterpretationBias ! Use And Interpretation Bias

[Term]
id: aio:FeedbackNetwork
name: Feedback Network
def: "An artificial neural network that refines its representations iteratively based on feedback from previous outputs." []
comment: Layers: Input, Hidden, Output, Hidden
subset: aio:NetworkSubset
synonym: "FBN" EXACT []
is_a: aio:ArtificialNeuralNetwork ! Artificial Neural Network
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:FixedEffectsModel
name: Fixed Effects Model
def: "A regression analysis in which the model parameters are fixed or non-random quantities." [https://en.wikipedia.org/wiki/Fixed_effects_model]
subset: aio:MachineLearningSubset
synonym: "FEM" EXACT []
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:FlattenLayer
name: Flatten Layer
def: "A reshaping layer that flattens the input" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten]
comment: Flattens the input. Does not affect the batch size.
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:FractionalMaxPool2DLayer
name: FractionalMaxPool2D Layer
def: "A pooling layer that applies a 2D fractional max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "FractionalMaxPool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:FractionalMaxPool3DLayer
name: FractionalMaxPool3D Layer
def: "A pooling layer that applies a 3D fractional max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "FractionalMaxPool3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:FundingBias
name: Funding Bias
def: "A group bias arising when biased results are reported to support or satisfy the funding agency or financial supporter of a research study." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:GroupBias ! Group Bias

[Term]
id: aio:GELUFunction
name: GELU Function
def: "A mathematical function that computes x * P(X <= x) where P(X) ~ N(0 1) weighting inputs by their value rather than gating inputs by their sign as in ReLU." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/gelu]
comment: Gaussian error linear unit (GELU) computes x * P(X <= x), where P(X) ~ N(0, 1). The (GELU) nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLU.
subset: aio:FunctionSubset
synonym: "Gaussian Error Linear Unit" EXACT []
synonym: "GELU" EXACT []
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:GRUCellLayer
name: GRUCell Layer
def: "A layer that processes one step within the whole time sequence input for a GRU layer." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRUCell]
comment: Cell class for the GRU layer. This class processes one step within the whole time sequence input, whereas tf.keras.layer.GRU processes the whole sequence.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:GRULayer
name: GRU Layer
def: "A recurrent layer that implements the Gated Recurrent Unit architecture." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU]
comment: Gated Recurrent Unit - Cho et al. 2014. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation. The requirements to use the cuDNN implementation are: activation == tanh, recurrent_activation == sigmoid, recurrent_dropout == 0, unroll is False, use_bias is True, reset_after is True. Inputs, if use masking, are strictly right-padded. Eager execution is enabled in the outermost context. There are two variants of the GRU implementation. The default one is based on v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original and has the order reversed. The second variant is compatible with CuDNNGRU (GPU-only) and allows inference on CPU. Thus it has separate biases for kernel and recurrent_kernel. To use this variant, set reset_after=True and recurrent_activation='sigmoid'.
subset: aio:LayerSubset
is_a: aio:RecurrentLayer ! Recurrent Layer

[Term]
id: aio:GatedRecurrentUnit
name: Gated Recurrent Unit
def: "A long short-term memory network that is a gating mechanism in recurrent neural networks similar to LSTMs but with fewer parameters and no output gate." [https://en.wikipedia.org/wiki/Gated_recurrent_unit]
comment: Layers: Input, Memory Cell, Output
subset: aio:NetworkSubset
synonym: "GRU" EXACT []
is_a: aio:LongShortTermMemory ! Long Short Term Memory

[Term]
id: aio:GaussianDropoutLayer
name: GaussianDropout Layer
def: "A regularization layer that applies multiplicative 1-centered Gaussian noise." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GaussianDropout]
comment: Apply multiplicative 1-centered Gaussian noise. As it is a regularization layer, it is only active at training time.
subset: aio:LayerSubset
is_a: aio:RegularizationLayer ! Regularization Layer

[Term]
id: aio:GaussianNoiseLayer
name: GaussianNoise Layer
def: "A regularization layer that applies additive zero-centered Gaussian noise." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GaussianNoise]
comment: Apply additive zero-centered Gaussian noise. This is useful to mitigate overfitting (you could see it as a form of random data augmentation). Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs. As it is a regularization layer, it is only active at training time.
subset: aio:LayerSubset
is_a: aio:RegularizationLayer ! Regularization Layer

[Term]
id: aio:GeneralizedAutoregressiveConditionalHeteroskedasticity
name: Generalized Autoregressive Conditional Heteroskedasticity
def: "A model that incorporates lagged conditional variances, allowing for more flexibility in modeling time-varying volatility." []
subset: aio:ModelSubset
synonym: "GARCH" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:GeneralizedFewshotLearning
name: Generalized Few-shot Learning
def: "A deep neural network that learns novel classes from few samples per class, preventing catastrophic forgetting of base classes and ensuring classifier calibration." [https://paperswithcode.com/paper/generalized-and-incremental-few-shot-learning/review/]
subset: aio:NetworkSubset
synonym: "GFSL" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:GeneralizedLinearModel
name: Generalized Linear Model
def: "A regression analysis that relates the linear model to the response variable via a link function and allowing the variance of each measurement to be a function of its predicted value." [https://en.wikipedia.org/wiki/Generalized_linear_model]
subset: aio:MachineLearningSubset
synonym: "GLM" EXACT []
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:GenerativeAdversarialNetwork
name: Generative Adversarial Network
def: "An unsupervised pretrained network framework where two neural networks contest in a game to generate new data with the same statistics as the training set." [https://en.wikipedia.org/wiki/Generative_adversarial_network]
comment: Layers: Backfed Input, Hidden, Matched Output-Input, Hidden, Matched Output-Input
subset: aio:NetworkSubset
synonym: "GAN" EXACT []
is_a: aio:UnsupervisedPretrainedNetwork ! Unsupervised Pretrained Network
relationship: BFO:0000051 aio:BackfedInputLayer ! has part Backfed Input Layer
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:MatchedInputOutputLayer ! has part Matched Input-Output Layer

[Term]
id: aio:GenerativeAdversarialNetworkAugmentedLLM
name: Generative Adversarial Network-Augmented LLM
def: "A large language model which incorporates a generative adversarial network (GAN) into its training process, using a discriminator network to provide a signal for generating more realistic and coherent text. This adversarial training can improve the quality and diversity of generated text." []
subset: aio:ModelSubset
synonym: "adversarial training" RELATED []
synonym: "GAN-Large Language Model" EXACT []
synonym: "Generative Adversarial Network-Augmented Large Language Model" EXACT []
synonym: "text generation" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:GenerativeCommonsenseLLM
name: Generative Commonsense LLM
def: "A large language model that is trained to understand and model basic physics causality and common sense about how the real world works." [https://arxiv.org/abs/2306.12672]
subset: aio:ModelSubset
synonym: "causal modeling" RELATED []
synonym: "Generative Commonsense Large Language Model" EXACT []
synonym: "physical reasoning" RELATED []
synonym: "World Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:GenerativeLanguageInterface
name: Generative Language Interface
def: "A language model that enables users to engage in an interactive dialogue with an LLM providing feedback to guide and refine the generated outputs iteratively." []
subset: aio:ModelSubset
synonym: "Interactive generation" RELATED []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:GlobalAveragePooling1DLayer
name: GlobalAveragePooling1D Layer
def: "A pooling layer that performs global average pooling operation for temporal data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D]
comment: Global average pooling operation for temporal data.
subset: aio:LayerSubset
synonym: "GlobalAvgPool1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:GlobalAveragePooling2DLayer
name: GlobalAveragePooling2D Layer
def: "A pooling layer that performs global average pooling operation for spatial data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling2D]
comment: Global average pooling operation for spatial data.
subset: aio:LayerSubset
synonym: "GlobalAvgPool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:GlobalAveragePooling3DLayer
name: GlobalAveragePooling3D Layer
def: "A pooling layer that performs global average pooling operation for 3D data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling3D]
comment: Global Average pooling operation for 3D data.
subset: aio:LayerSubset
synonym: "GlobalAvgPool3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:GlobalMaxPooling1DLayer
name: GlobalMaxPooling1D Layer
def: "A pooling layer that performs global max pooling operation for temporal data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalMaxPool1D]
comment: Global max pooling operation for 1D temporal data.
subset: aio:LayerSubset
synonym: "GlobalMaxPool1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:GlobalMaxPooling2DLayer
name: GlobalMaxPooling2D Layer
def: "A pooling layer that performs global max pooling operation for spatial data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalMaxPool2D]
comment: Global max pooling operation for spatial data.
subset: aio:LayerSubset
synonym: "GlobalMaxPool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:GlobalMaxPooling3DLayer
name: GlobalMaxPooling3D Layer
def: "A pooling layer that performs global max pooling operation for 3D data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalMaxPool3D]
comment: Global Max pooling operation for 3D data.
subset: aio:LayerSubset
synonym: "GlobalMaxPool3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:GraphConvolutionalNetwork
name: Graph Convolutional Network
def: "A deep neural network that operates directly on graph structures utilizing structural information." [https://arxiv.org/abs/1609.02907]
comment: Layers: Input, Hidden, Hidden, Output
subset: aio:NetworkSubset
synonym: "GCN" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:GraphConvolutionalPolicyNetwork
name: Graph Convolutional Policy Network
def: "A graph convolutional network that generates goal-directed graphs using reinforcement learning and optimizing for rewards and adversarial loss." [https://arxiv.org/abs/1806.02473]
comment: Layers: Input, Hidden, Hidden, Policy, Output
subset: aio:NetworkSubset
synonym: "GPCN" EXACT []
is_a: aio:GraphConvolutionalNetwork ! Graph Convolutional Network
relationship: BFO:0000051 aio:PolicyLayer ! has part Policy Layer

[Term]
id: aio:GraphLanguageModel
name: Graph Language Model
def: "A language model that operates over structured inputs or outputs represented as graphs enabling reasoning over explicit relational knowledge representations during language tasks." [https://arxiv.org/abs/2401.07105]
subset: aio:ModelSubset
synonym: "Graph LM" EXACT []
synonym: "Structured representations" RELATED []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:GroupBias
name: Group Bias
def: "A systemic bias characterized by favoring members of one's in-group over out-group members expressed in evaluation resource allocation and other ways." [https://en.wikipedia.org/wiki/In-group_favoritism]
comment: Favoring members of one's in-group over out-group members, expressed in evaluation, resource allocation, and other ways.
subset: aio:BiasSubset
synonym: "In-group bias" EXACT []
synonym: "In-group Favoritism" EXACT []
synonym: "In-group preference" EXACT []
synonym: "In-group–out-group Bias" EXACT []
synonym: "Intergroup bias" EXACT []
is_a: aio:SystemicBias ! Systemic Bias

[Term]
id: aio:GroupNormLayer
name: GroupNorm Layer
def: "A normalization layer that applies Group Normalization over a mini-batch of inputs." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization
subset: aio:LayerSubset
synonym: "GroupNorm" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:GroupthinkBias
name: Groupthink Bias
def: "A group bias in which people in a group make non-optimal decisions due to a desire to conform or fear of dissent." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Groupthink" EXACT []
is_a: aio:GroupBias ! Group Bias

[Term]
id: aio:HardSigmoidFunction
name: Hard Sigmoid Function
def: "A mathematical function that is a faster approximation of the sigmoid activation using a piecewise linear approximation." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/hard_sigmoid]
comment: A faster approximation of the sigmoid activation. Piecewise linear approximation of the sigmoid function. Ref: 'https://en.wikipedia.org/wiki/Hard_sigmoid'
subset: aio:FunctionSubset
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:HashingLayer
name: Hashing Layer
def: "A categorical features preprocessing layer which hashes and bins categorical features." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Hashing]
comment: A preprocessing layer which hashes and bins categorical features. This layer transforms categorical inputs to hashed output. It element-wise converts a ints or strings to ints in a fixed range. The stable hash function uses tensorflow::ops::Fingerprint to produce the same output consistently across all platforms. This layer uses FarmHash64 by default, which provides a consistent hashed output across different platforms and is stable across invocations, regardless of device and context, by mixing the input bits thoroughly. If you want to obfuscate the hashed output, you can also pass a random salt argument in the constructor. In that case, the layer will use the SipHash64 hash function, with the salt value serving as additional input to the hash function.
subset: aio:LayerSubset
is_a: aio:CategoricalFeaturesPreprocessingLayer ! Categorical Features Preprocessing Layer

[Term]
id: aio:HiddenLayer
name: Hidden Layer
def: "A layer located between the input and output that performs nonlinear transformations of the inputs entered into the network." [https://deepai.org/machine-Learning-glossary-and-terms/hidden-layer-machine-Learning]
comment: A hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network. Hidden layers vary depending on the function of the neural network, and similarly, the layers may vary depending on their associated weights.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:HierarchicalClassification
name: Hierarchical Classification
def: "A classification focused on methods that group things according to a hierarchy." [https://en.wikipedia.org/wiki/Hierarchical_classification]
comment: Methods that group things according to a hierarchy.
subset: aio:MachineLearningSubset
is_a: aio:Classification ! Classification

[Term]
id: aio:HierarchicalClustering
name: Hierarchical Clustering
def: "A clustering that builds a hierarchy of clusters." [https://en.wikipedia.org/wiki/Hierarchical_clustering]
comment: Methods that build a hierarchy of clusters.
subset: aio:MachineLearningSubset
synonym: "HCL" EXACT []
is_a: aio:Clustering ! Clustering

[Term]
id: aio:HierarchicalLanguageModel
name: Hierarchical Language Model
def: "A language model that represents language at multiple levels of granularity learning hierarchical representations that capture both low-level patterns and high-level abstractions." [https://doi.org/10.1016/j.ipm.2024.103698]
subset: aio:ModelSubset
synonym: "Hierarchical LM" EXACT []
synonym: "multi-scale representations" RELATED []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:HistoricalBias
name: Historical Bias
def: "A bias characterized by long-standing biases encoded in society over time distinct from biases in historical description or interpretation." [https://doi.org/10.6028/NIST.SP.1270]
comment: Long-standing biases encoded in society over time, distinct from biases in historical description or the interpretation of history, such as viewing the larger world from a Western or European perspective.
subset: aio:BiasSubset
is_a: aio:Bias ! Bias

[Term]
id: aio:HopfieldNetwork
name: Hopfield Network
def: "A symmetrically connected network that is a type of recurrent artificial neural network serving as a content-addressable memory system." [https://en.wikipedia.org/wiki/Hopfield_network]
comment: Layers: Backfed input
subset: aio:NetworkSubset
synonym: "HN" EXACT []
synonym: "Ising model of a neural network" EXACT []
synonym: "Ising–Lenz–Little model" EXACT []
is_a: aio:SymmetricallyConnectedNetwork ! Symmetrically Connected Network
relationship: BFO:0000051 aio:BackfedInputLayer ! has part Backfed Input Layer

[Term]
id: aio:HostileAttributionBias
name: Hostile Attribution Bias
def: "A use and interpretation bias where individuals perceive benign or ambiguous behaviors as hostile." [https://en.wikipedia.org/wiki/Interpretive_bias]
comment: Bias where individuals perceive benign or ambiguous behaviors as hostile.
subset: aio:BiasSubset
is_a: aio:UseAndInterpretationBias ! Use And Interpretation Bias

[Term]
id: aio:HumanBias
name: Human Bias
def: "A bias in human thought based on heuristic principles leading to simplified judgmental operations." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:Bias ! Bias

[Term]
id: aio:HumanReportingBias
name: Human Reporting Bias
def: "An individual bias that arises when users depend on automated systems as heuristic substitutes for their own information-seeking and processing efforts." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:ImageAugmentationLayer
name: Image Augmentation Layer
def: "A layer that performs image data preprocessing augmentations." [https://keras.io/guides/preprocessing_layers/]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:ImagePreprocessingLayer
name: Image Preprocessing Layer
def: "A layer that performs image data preprocessing operations." [https://keras.io/guides/preprocessing_layers/]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:ImplicitBias
name: Implicit Bias
def: "An individual bias characterized by unconscious beliefs attitudes feelings associations or stereotypes that affect information processing decision-making and actions." [https://doi.org/10.6028/NIST.SP.1270]
comment: Unconscious beliefs, attitudes, feelings, associations, or stereotypes that affect information processing, decision-making, and actions.
subset: aio:BiasSubset
synonym: "Confirmatory Bias" EXACT []
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:ImplicitLanguageModel
name: Implicit Language Model
def: "A language model that uses an energy function to score entire sequences instead of factorizing probabilities autoregressively better capturing global properties and long-range dependencies." [https://arxiv.org/pdf/2303.16189]
subset: aio:ModelSubset
synonym: "Energy-based models" RELATED []
synonym: "Implicit LM" EXACT []
synonym: "Token-level scoring" RELATED []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:IncremenetalFewshotLearning
name: Incremenetal Few-shot Learning
def: "A deep neural network trained on a base set of classes and then presented with novel classes, each with few labeled examples." [https://arxiv.org/abs/1810.07218]
subset: aio:NetworkSubset
synonym: "IFSL" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:IndividualBias
name: Individual Bias
def: "A bias characterized by a persistent point of view or limited list of such points of view, applied by an individual." [https://develop.consumerium.org/wiki/Individual_bias]
subset: aio:BiasSubset
is_a: aio:Bias ! Bias

[Term]
id: aio:InheritedBias
name: Inherited Bias
def: "A processing bias arising when machine learning applications generate inputs for other machine learning algorithms passing on any existing bias." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:ProcessingBias ! Processing Bias

[Term]
id: aio:InputLayer
name: Input Layer
def: "A layer composed of artificial input neurons that brings the initial data into the system for further processing by subsequent layers." [https://www.techopedia.com/definition/33262/input-layer-neural-networks]
comment: The input layer of a neural network is composed of artificial input neurons, and brings the initial data into the system for further processing by subsequent layers of artificial neurons. The input layer is the very beginning of the workflow for the artificial neural network.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:InputLayerLayer
name: InputLayer Layer
def: "A layer to be used as an entry point into a Network (a graph of layers)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/InputLayer]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:InputSpecLayer
name: InputSpec Layer
def: "A layer that specifies the rank" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/InputSpec]
comment: Specifies the rank, dtype and shape of every input to a layer. Layers can expose (if appropriate) an input_spec attribute: an instance of InputSpec, or a nested structure of InputSpec instances (one per input tensor). These objects enable the layer to run input compatibility checks for input structure, input rank, input shape, and input dtype. A None entry in a shape is compatible with any dimension, a None shape is compatible with any shape.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:InstanceNorm1DLayer
name: InstanceNorm1D Layer
def: "A normalization layer that applies Instance Normalization over a 2D (unbatched) or 3D (batched) input." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Instance Normalization over a 2D (unbatched) or 3D (batched) input as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.
subset: aio:LayerSubset
synonym: "InstanceNorm1D" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:InstanceNorm2D
name: InstanceNorm2D
def: "A normalization layer that applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension)." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.
subset: aio:LayerSubset
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:InstanceNorm3DLayer
name: InstanceNorm3D Layer
def: "A normalization layer that applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension)." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.
subset: aio:LayerSubset
synonym: "InstanceNorm3D" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:InstitutionalBias
name: Institutional Bias
def: "A systemic bias exhibited at the level of entire institutions where practices or norms result in the favoring or disadvantaging of certain social groups." [https://doi.org/10.6028/NIST.SP.1270]
comment: Bias exhibited at the level of entire institutions, where practices or norms result in the favoring or disadvantaging of certain social groups, such as institutional racism or sexism.
subset: aio:BiasSubset
is_a: aio:SystemicBias ! Systemic Bias

[Term]
id: aio:InstructionTunedLLM
name: Instruction-Tuned LLM
def: "A large language model which is fine-tuned to follow natural language instructions accurately and safely, learning to map from instructions to desired model behavior in a more controlled and principled way." []
subset: aio:ModelSubset
synonym: "constitutional AI" RELATED []
synonym: "Instruction-Tuned Large Language Model" EXACT []
synonym: "natural language instructions" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:IntegerLookupLayer
name: IntegerLookup Layer
def: "A categorical features preprocessing layer that maps integer features to contiguous ranges." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/IntegerLookup]
subset: aio:LayerSubset
is_a: aio:CategoricalFeaturesPreprocessingLayer ! Categorical Features Preprocessing Layer

[Term]
id: aio:InterpretationBias
name: Interpretation Bias
def: "An individual bias where users interpret algorithmic outputs according to their internalized biases and views." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:KernelLayer
name: Kernel Layer
def: "A layer that obtains the dot product of input values or subsets of input values." []
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:KnearestNeighborAlgorithm
name: K-nearest Neighbor Algorithm
def: "A machine learning task that groups objects by a plurality vote of its neighbors, assigning each object to the class most common among its k nearest neighbors." [https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]
subset: aio:MachineLearningSubset
synonym: "K-NN" EXACT []
synonym: "KNN" EXACT []
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:KnearestNeighborClassificationAlgorithm
name: K-nearest Neighbor Classification Algorithm
def: "A classification and clustering that classifies objects by a plurality vote of its neighbors, assigning each object to the class most common among its k nearest neighbors." [https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]
subset: aio:MachineLearningSubset
synonym: "K-NN Classification" EXACT []
synonym: "KNN Classification" EXACT []
is_a: aio:Clustering ! Clustering

[Term]
id: aio:KnearestNeighborRegressionAlgorithm
name: K-nearest Neighbor Regression Algorithm
def: "An regression analysis that assigns the average of the values of k nearest neighbors to objects." [https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]
subset: aio:MachineLearningSubset
synonym: "K-NN Regression" EXACT []
synonym: "KNN Regression" EXACT []
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:KnowledgeGroundedLLM
name: Knowledge-Grounded LLM
def: "A large language model which incorporates external knowledge sources or knowledge bases into the model architecture, enabling it to generate more factually accurate and knowledge-aware text." []
subset: aio:ModelSubset
synonym: "factual grounding" RELATED []
synonym: "knowledge integration" RELATED []
synonym: "Knowledge-Grounded Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:KnowledgeTransfer
name: Knowledge Transfer
def: "A training strategy in which knowledge is passed from one entity such as a person organization or system to another facilitating learning and adaptation in the receiving entity through various methods such as teaching training or data exchange." [https://doi.org/10.1016/j.knosys.2015.01.010]
subset: aio:TrainingStrategySubset
synonym: "Adaptation" RELATED []
synonym: "Inductive Transfer" EXACT []
synonym: "Pretrained models" RELATED []
is_a: aio:TrainingStrategy ! TrainingStrategy

[Term]
id: aio:KohonenNetwork
name: Kohonen Network
def: "A network that is an unsupervised technique producing a low-dimensional representation of high-dimensional data preserving topological structure." [https://en.wikipedia.org/wiki/Self-organizing_map]
comment: Layers: Input, Hidden
subset: aio:NetworkSubset
synonym: "KN" EXACT []
synonym: "Self-Organizing Feature Map" EXACT []
synonym: "Self-Organizing Map" EXACT []
synonym: "SOFM" EXACT []
synonym: "SOM" EXACT []
is_a: aio:Network ! Network
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer

[Term]
id: aio:LPPool1DLayer
name: LPPool1D Layer
def: "A pooling layer that applies 1D power-average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "LPPool1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:LPPool2DLayer
name: LPPool2D Layer
def: "A pooling layer that applies 2D power-average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
subset: aio:LayerSubset
synonym: "LPPool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:LSTMCellLayer
name: LSTMCell Layer
def: "A layer that processes one step within the whole time sequence input for an LSTM layer." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTMCell]
comment: Cell class for the LSTM layer.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:LSTMLayer
name: LSTM Layer
def: "A recurrent layer that implements the Long Short-Term Memory architecture." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM]
comment: Long Short-Term Memory layer - Hochreiter 1997. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation. The requirements to use the cuDNN implementation are: 1. activation == tanh, 2. recurrent_activation == sigmoid, 3. recurrent_dropout == 0, 4. unroll is False, 5. use_bias is True, 6. Inputs, if use masking, are strictly right-padded, 7. Eager execution is enabled in the outermost context.
subset: aio:LayerSubset
is_a: aio:RecurrentLayer ! Recurrent Layer

[Term]
id: aio:LambdaLayer
name: Lambda Layer
def: "A layer that wraps arbitrary expressions as a Layer object." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Lambda]
comment: Wraps arbitrary expressions as a Layer object.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:LanguageInterfaceLLM
name: Language Interface LLM
def: "A large language model that supports interactive semantic parsing enabling users to provide feedback and corrections to dynamically refine and update the language model." []
subset: aio:ModelSubset
synonym: "Interactive learning" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:LanguageModel
name: Language Model
def: "A model designed to predict the next word in a sequence or assign probabilities to sequences of words in natural language." [https://en.wikipedia.org/wiki/Language_model]
subset: aio:ModelSubset
is_a: aio:Model ! Model

[Term]
id: aio:LargeLanguageModel
name: Large Language Model
def: "A language model consisting of a neural network with many parameters (typically billions of weights or more) trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning." [https://en.wikipedia.org/wiki/Large_language_model]
subset: aio:ModelSubset
synonym: "LLM" EXACT []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:LassoRegression
name: Lasso Regression
def: "A regression analysis that performs both variable selection and regularization to enhance prediction accuracy and interpretability." [https://en.wikipedia.org/wiki/Lasso_(statistics)]
subset: aio:MachineLearningSubset
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:Layer
name: Layer
def: "A structure or network topology in a deep learning model that takes information from previous layers and passes it to the next layer." [https://en.wikipedia.org/wiki/Layer_(deep_learning)]
subset: aio:ClassSubset

[Term]
id: aio:LayerLayer
name: Layer Layer
def: "A layer that form which other layers can inherit." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer]
comment: This is the class from which all layers inherit. A layer is a callable object that takes as input one or more tensors and that outputs one or more tensors. It involves computation, defined in the call() method, and a state (weight variables). State can be created in various places, at the convenience of the subclass implementer: in __init__(); in the optional build() method, which is invoked by the first __call__() to the layer, and supplies the shape(s) of the input(s), which may not have been known at initialization time; in the first invocation of call(), with some caveats discussed below. Users will just instantiate a layer and then treat it as a callable.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:LayerNormLayer
name: LayerNorm Layer
def: "A normalization layer that applies Layer Normalization over a mini-batch of inputs." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization
subset: aio:LayerSubset
synonym: "LayerNorm" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:LayerNormalizationLayer
name: LayerNormalization Layer
def: "A normalization layer that applies Layer Normalization over the inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LayerNormalization]
comment: Layer normalization layer (Ba et al., 2016). Normalize the activations of the previous layer for each given example in a batch independently, rather than across a batch like Batch Normalization. i.e. applies a transformation that maintains the mean activation within each example close to 0 and the activation standard deviation close to 1. Given a tensor inputs, moments are calculated and normalization is performed across the axes specified in axis.
subset: aio:LayerSubset
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:LazyBatchNorm1DLayer
name: LazyBatchNorm1D Layer
def: "A batch normalization layer that lazily initializes the num_features argument from the input size for 1D data." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: A torch.nn.BatchNorm1D module with lazy initialization of the num_features argument of the BatchNorm1D that is inferred from the input.size(1).
subset: aio:LayerSubset
synonym: "LazyBatchNorm1D" EXACT []
is_a: aio:BatchNormalizationLayer ! BatchNormalization Layer

[Term]
id: aio:LazyBatchNorm2DLayer
name: LazyBatchNorm2D Layer
def: "A batch normalization layer that lazily initializes the num_features argument from the input size for 2D data." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: A torch.nn.BatchNorm2D module with lazy initialization of the num_features argument of the BatchNorm2D that is inferred from the input.size(1).
subset: aio:LayerSubset
synonym: "LazyBatchNorm2D" EXACT []
is_a: aio:BatchNormalizationLayer ! BatchNormalization Layer

[Term]
id: aio:LazyBatchNorm3DLayer
name: LazyBatchNorm3D Layer
def: "A batch normalization layer that lazily initializes the num_features argument from the input size for 3D data." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: A torch.nn.BatchNorm3D module with lazy initialization of the num_features argument of the BatchNorm3D that is inferred from the input.size(1).
subset: aio:LayerSubset
synonym: "LazyBatchNorm3D" EXACT []
is_a: aio:BatchNormalizationLayer ! BatchNormalization Layer

[Term]
id: aio:LazyInstanceNorm1DLayer
name: LazyInstanceNorm1D Layer
def: "A normalization layer that lazily initializes the num_features argument from the input size for 1D data." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: A torch.nn.InstanceNorm1D module with lazy initialization of the num_features argument of the InstanceNorm1D that is inferred from the input.size(1).
subset: aio:InstanceNormalizationLayerSubset
subset: aio:LayerSubset
synonym: "LazyInstanceNorm1D" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:LazyInstanceNorm2DLayer
name: LazyInstanceNorm2D Layer
def: "A normalization layer that lazily initializes the num_features argument from the input size for 2D data." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: A torch.nn.InstanceNorm2D module with lazy initialization of the num_features argument of the InstanceNorm2D that is inferred from the input.size(1).
subset: aio:InstanceNormalizationLayerSubset
subset: aio:LayerSubset
synonym: "LazyInstanceNorm2D" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:LazyInstanceNorm3DLayer
name: LazyInstanceNorm3D Layer
def: "A normalization that lazily initializes the num_features argument from the input size for 3D data." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: A torch.nn.InstanceNorm3D module with lazy initialization of the num_features argument of the InstanceNorm3D that is inferred from the input.size(1).
subset: aio:InstanceNormalizationLayerSubset
subset: aio:LayerSubset
synonym: "LazyInstanceNorm3D" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:LeakyReLULayer
name: LeakyReLU Layer
def: "An activation layer that applies the leaky rectified linear unit function element-wise." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU]
comment: Leaky version of a Rectified Linear Unit.
subset: aio:LayerSubset
is_a: aio:ActivationLayer ! Activation Layer

[Term]
id: aio:LeastsquaresAnalysis
name: Least-squares Analysis
def: "A regression analysis which approximates the solution of overdetermined systems by minimizing the sum of the squares of the residuals." [https://en.wikipedia.org/wiki/Least_squares]
subset: aio:MachineLearningSubset
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:LifelongLearningLLM
name: Lifelong Learning LLM
def: "A large language model that continually acquires new knowledge over time without forgetting previously learned information maintaining a balance between plasticity and stability." []
subset: aio:ModelSubset
synonym: "Catastrophic forgetting" RELATED []
synonym: "Continual Learning LLM" EXACT []
synonym: "Forever Learning" EXACT []
synonym: "Plasticity-Stability balance" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:LinearFunction
name: Linear Function
def: "A mathematical function that has the form f(x) = a + bx." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/linear]
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:LinearRegression
name: Linear Regression
def: "A regression analysis that is a linear approach for modeling the relationship between a scalar response and one or more explanatory variables." [https://en.wikipedia.org/wiki/Linear_regression]
subset: aio:MachineLearningSubset
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:LinkingBias
name: Linking Bias
def: "A use and interpretation bias arising when network attributes obtained from user connections activities or interactions misrepresent true user behavior." [https://doi.org/10.6028/NIST.SP.1270]
comment: Bias arising when network attributes obtained from user connections, activities, or interactions misrepresent true user behavior.
subset: aio:BiasSubset
is_a: aio:UseAndInterpretationBias ! Use And Interpretation Bias

[Term]
id: aio:LiquidStateMachineNetwork
name: Liquid State Machine Network
def: "A network that is a type of reservoir computer turning time-varying input into spatio-temporal activation patterns." [https://en.wikipedia.org/wiki/Liquid_state_machine]
comment: Layers: Input, Spiking Hidden, Output
subset: aio:NetworkSubset
synonym: "LSM" EXACT []
is_a: aio:Network ! Network
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer
relationship: BFO:0000051 aio:SpikingHiddenLayer ! has part Spiking Hidden Layer

[Term]
id: aio:LocalResponseNormLayer
name: LocalResponseNorm Layer
def: "A normalization layer that applies local response normalization over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension.
subset: aio:LayerSubset
synonym: "LocalResponseNorm" EXACT []
is_a: aio:NormalizationLayer ! Normalization Layer

[Term]
id: aio:LocallyConnected1DLayer
name: LocallyConnected1D Layer
def: "A locally-connected layer for 1D inputs where each patch of the input is convolved with a different set of filters." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LocallyConnected1D]
comment: Locally-connected layer for 1D inputs. The LocallyConnected1D layer works similarly to the Conv1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.
subset: aio:LayerSubset
is_a: aio:LocallyconnectedLayer ! Locally-connected Layer

[Term]
id: aio:LocallyConnected2DLayer
name: LocallyConnected2D Layer
def: "A locally-connected layer for 2D inputs where each patch of the input is convolved with a different set of filters." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LocallyConnected2D]
comment: Locally-connected layer for 2D inputs. The LocallyConnected2D layer works similarly to the Conv2D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.
subset: aio:LayerSubset
is_a: aio:LocallyconnectedLayer ! Locally-connected Layer

[Term]
id: aio:LocallyconnectedLayer
name: Locally-connected Layer
def: "A layer that works similarly to the Convolution1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input." [https://faroit.com/keras-docs/1.2.2/layers/local/]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:LogisticRegression
name: Logistic Regression
def: "A regression analysis that estimates the probability of an event occurring by modeling the log-odds of the event as a linear combination of one or more independent variables." [https://en.wikipedia.org/wiki/Logistic_regression]
subset: aio:MachineLearningSubset
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:LongShortTermMemory
name: Long Short Term Memory
def: "A recurrent neural network with feedback connections that processes entire sequences of data." [https://en.wikipedia.org/wiki/Long_short-term_memory]
comment: Layers: Input, Memory Cell, Output
subset: aio:NetworkSubset
synonym: "LSTM" EXACT []
is_a: aio:RecurrentNeuralNetwork ! Recurrent Neural Network
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:MemoryCellLayer ! has part Memory Cell Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:LossOfSituationalAwarenessBias
name: Loss Of Situational Awareness Bias
def: "An individual bias occurring when automation leads to humans being unaware of their situation making them unprepared to assume control in cooperative systems." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:LowResourceLLM
name: Low-Resource LLM
def: "A large language model which is optimized for performance in scenarios with limited data, computational resources, or for languages with sparse datasets." []
subset: aio:ModelSubset
synonym: "Low-Resource Language Model" EXACT []
synonym: "low-resource languages" RELATED []
synonym: "resource-efficient" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:MachineLearningTask
name: Machine Learning Task
def: "A process intended to build methods that learn from data." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:MachineLearningSubset
synonym: "Machine Learning" RELATED []

[Term]
id: aio:ManifoldLearning
name: Manifold Learning
def: "A dimensionality reduction method based on the assumption that observed data lie on a low-dimensional manifold embedded in a higher-dimensional space." [https://arxiv.org/abs/2011.01307]
subset: aio:MachineLearningSubset
is_a: aio:DimensionalityReduction ! Dimensionality Reduction

[Term]
id: aio:MarkovChain
name: Markov Chain
def: "A network that is a stochastic model describing a sequence of possible events where the probability of each event depends only on the previous event's state." [https://en.wikipedia.org/wiki/Markov_chain]
comment: Layers: Probalistic Hidden
subset: aio:ModelSubset
synonym: "Markov Process" EXACT []
synonym: "MC" EXACT []
synonym: "MP" EXACT []
is_a: aio:Network ! Network
relationship: BFO:0000051 aio:ProbabilisticHiddenLayer ! has part Probabilistic Hidden Layer

[Term]
id: aio:MaskedLanguageModel
name: Masked Language Model
def: "A language model that is trained to predict randomly masked tokens in a sequence based on the remaining unmasked tokens allowing it to build deep bidirectional representations that can be effectively transferred to various NLP tasks via fine-tuning." []
subset: aio:ModelSubset
synonym: "bidirectional encoder" RELATED []
synonym: "denoising autoencoder" RELATED []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:MaskingLayer
name: Masking Layer
def: "A layer that masks a sequence by using a mask value to skip timesteps." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Masking]
comment: Masks a sequence by using a mask value to skip timesteps. For each timestep in the input tensor (dimension #1 in the tensor), if all values in the input tensor at that timestep are equal to mask_value, then the timestep will be masked (skipped) in all downstream layers (as long as they support masking). If any downstream layer does not support masking yet receives such an input mask, an exception will be raised.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:MatchedInputOutputLayer
name: Matched Input-Output Layer
def: "An input layer with a shape corresponding to that of the output layer." []
subset: aio:LayerSubset
is_a: aio:InputLayer ! Input Layer

[Term]
id: aio:MathematicalFunction
name: Mathematical Function
def: "A mathematical rule that gives the value of a dependent variable corresponding to specified values of one or more independent variables." [https://www.sciencedirect.com/topics/mathematics/mathematical-function]
subset: aio:ClassSubset

[Term]
id: aio:MaxPooling1DLayer
name: MaxPooling1D Layer
def: "A pooling layer that performs max pooling operation for temporal data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool1D]
comment: Max pooling operation for 1D temporal data. Downsamples the input representation by taking the maximum value over a spatial window of size pool_size. The window is shifted by strides. The resulting output, when using the "valid" padding option, has a shape of: output_shape = (input_shape - pool_size + 1) / strides) The resulting output shape when using the "same" padding option is: output_shape = input_shape / strides.
subset: aio:LayerSubset
synonym: "MaxPool1D" EXACT []
synonym: "MaxPooling1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:MaxPooling2DLayer
name: MaxPooling2D Layer
def: "A pooling layer that performs max pooling operation for spatial data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D]
comment: Max pooling operation for 2D spatial data.
subset: aio:LayerSubset
synonym: "MaxPool2D" EXACT []
synonym: "MaxPooling2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:MaxPooling3DLayer
name: MaxPooling3D Layer
def: "A pooling layer that performs max pooling operation for 3D data (spatial or spatio-temporal)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool3D]
comment: Max pooling operation for 3D data (spatial or spatio-temporal). Downsamples the input along its spatial dimensions (depth, height, and width) by taking the maximum value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension.
subset: aio:LayerSubset
synonym: "MaxPool3D" EXACT []
synonym: "MaxPooling3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:MaxUnpool1DLayer
name: MaxUnpool1D Layer
def: "A pooling layer that computes a partial inverse of MaxPool1D." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
comment: Computes a partial inverse of MaxPool1D.
subset: aio:LayerSubset
synonym: "MaxUnpool1D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:MaxUnpool2DLayer
name: MaxUnpool2D Layer
def: "A pooling layer that computes a partial inverse of MaxPool2D." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
comment: Computes a partial inverse of MaxPool2D.
subset: aio:LayerSubset
synonym: "MaxUnpool2D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:MaxUnpool3DLayer
name: MaxUnpool3D Layer
def: "A pooling layer that computes a partial inverse of MaxPool3D." [https://pytorch.org/docs/stable/nn.html#pooling-layers]
comment: Computes a partial inverse of MaxPool3D.
subset: aio:LayerSubset
synonym: "MaxUnpool3D" EXACT []
is_a: aio:PoolingLayer ! Pooling Layer

[Term]
id: aio:MaximumLayer
name: Maximum Layer
def: "A merging layer that computes the maximum (element-wise) of a list of inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Maximum]
comment: Layer that computes the maximum (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
subset: aio:LayerSubset
is_a: aio:MergingLayer ! Merging Layer

[Term]
id: aio:MeasurementBias
name: Measurement Bias
def: "A selection and sampling bias arising when features and labels are proxies for desired quantities potentially leading to differential performance." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:MemoryAugmentedLLM
name: Memory-Augmented LLM
def: "A large language model which incorporates external writable and readable memory components, allowing it to store and retrieve information over long contexts." [https://arxiv.org/abs/2306.07174]
subset: aio:ModelSubset
synonym: "external memory" RELATED []
synonym: "Memory-Augmented Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:MemoryCellLayer
name: Memory Cell Layer
def: "A layer of cells, each with an internal state or weights." [https://doi.org/10.1162/neco.1997.9.8.1735]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:MergingLayer
name: Merging Layer
def: "A layer used to merge a list of inputs." [https://www.tutorialspoint.com/keras/keras_merge_layer.htm]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:MetaLearning
name: Meta-Learning
def: "A machine learning task that automatically learns from metadata about machine learning experiments." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:NetworkSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:MetaLearningLLM
name: Meta-Learning LLM
def: "A large language model which is trained in a way that allows it to quickly adapt to new tasks or datasets through only a few examples or fine-tuning steps, leveraging meta-learned priors about how to efficiently learn." []
subset: aio:ModelSubset
synonym: "few-shot adaptation" RELATED []
synonym: "learning to learn" RELATED []
synonym: "Meta-Learning Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:MetricLearning
name: Metric Learning
def: "A deep neural network that learns a representation function mapping objects into an embedded space." [https://paperswithcode.com/task/metric-learning]
comment: Learning a representation function that maps objects into an embedded space.
subset: aio:NetworkSubset
synonym: "Distance Metric Learning" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:MinimumLayer
name: Minimum Layer
def: "A merging layer that computes the minimum (element-wise) of a list of inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Minimum]
comment: Layer that computes the minimum (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
subset: aio:LayerSubset
is_a: aio:MergingLayer ! Merging Layer

[Term]
id: aio:MixtureofExpertsLLM
name: Mixture-of-Experts LLM
def: "A large language model which dynamically selects and combines outputs from multiple expert submodels, allowing for efficient scaling by conditionally activating only a subset of model components for each input." [https://proceedings.mlr.press/v162/du22c.html]
subset: aio:ModelSubset
synonym: "conditional computation" RELATED []
synonym: "Mixture-of-Experts Large Language Model" EXACT []
synonym: "model parallelism" RELATED []
synonym: "MoE Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ModeConfusionBias
name: Mode Confusion Bias
def: "An individual bias occurring when modal interfaces confuse human operators causing actions appropriate for a different mode but incorrect for the current situation." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:Model
name: Model
def: "An abstract representation of a complex system generally assembled as a set of logical mathematical or conceptual properties to simulate or understand the system's behavior." [https://en.wikipedia.org/wiki/Mathematical_model]
subset: aio:ModelSubset

[Term]
id: aio:ModelSelectionBias
name: Model Selection Bias
def: "A processing bias introduced when using data to select a single \"best\" model from many or when an explanatory variable has a weak relationship with the response variable." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:ProcessingBias ! Processing Bias

[Term]
id: aio:ModularLLM
name: Modular LLM
def: "A modular language model that consists of multiple specialized components or skills that can be dynamically composed and recombined to solve complex tasks mimicking the modular structure of human cognition." [https://arxiv.org/abs/2302.11529v2]
subset: aio:ModelSubset
synonym: "component skills" RELATED []
synonym: "Modular Large Language Model" EXACT []
synonym: "skill composition" RELATED []
is_a: aio:ModularLanguageModel ! Modular Language Model

[Term]
id: aio:ModularLanguageModel
name: Modular Language Model
def: "A language model that consists of multiple specialized components or skills that can be dynamically composed and recombined to solve complex tasks mimicking the modular structure of human cognition." [https://arxiv.org/abs/2302.11529v2]
subset: aio:ModelSubset
synonym: "Modular LM" EXACT []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:MultiHeadAttentionLayer
name: MultiHeadAttention Layer
def: "An attention layer that allows the model to attend to information from different representation subspaces." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MultiHeadAttention]
comment: MultiHeadAttention layer. This is an implementation of multi-headed attention as described in the paper "Attention is all you Need" (Vaswani et al., 2017). If query, key, value are the same, then this is self-attention. Each timestep in query attends to the corresponding sequence in key, and returns a fixed-width vector.This layer first projects query, key and value. These are (effectively) a list of tensors of length num_attention_heads, where the corresponding shapes are (batch_size, <query dimensions>, key_dim), (batch_size, <key/value dimensions>, key_dim), (batch_size, <key/value dimensions>, value_dim).Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor. Finally, the result tensor with the last dimension as value_dim can take an linear projection and return. When using MultiHeadAttention inside a custom Layer, the custom Layer must implement build() and call MultiHeadAttention's _build_from_signature(). This enables weights to be restored correctly when the model is loaded.
subset: aio:LayerSubset
is_a: aio:AttentionLayer ! Attention Layer

[Term]
id: aio:MultiTaskLLM
name: Multi-Task LLM
def: "A large language model which is trained jointly on multiple language tasks simultaneously, learning shared representations that transfer across tasks." []
subset: aio:ModelSubset
synonym: "Multi-Task Large Language Model" EXACT []
synonym: "transfer learning" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:MulticlassClassification
name: Multiclass Classification
def: "A classification focused on methods that classify instances into one of three or more classes." [https://en.wikipedia.org/wiki/Multiclass_classification]
comment: Methods that classify instances into one of three or more classes.
subset: aio:MachineLearningSubset
synonym: "Multinomial Classification" EXACT []
is_a: aio:Classification ! Classification

[Term]
id: aio:MultidimensionalScaling
name: Multidimensional Scaling
def: "A dimensionality reduction method that translates information about the pairwise distances among a set of objects or individuals into a configuration of points mapped into an abstract Cartesian space." [https://en.wikipedia.org/wiki/Multidimensional_scaling]
subset: aio:MachineLearningSubset
synonym: "MDS" EXACT []
is_a: aio:DimensionalityReduction ! Dimensionality Reduction

[Term]
id: aio:MultilingualLLM
name: Multilingual LLM
def: "A large language model that is trained on text from multiple languages learning shared representations that enable zero-shot or few-shot transfer to new languages." []
subset: aio:ModelSubset
synonym: "cross-lingual transfer" RELATED []
synonym: "Multilingual Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:MultimodalDeepLearning
name: Multimodal Deep Learning
def: "A deep neural network that processes and links information using various modalities." [https://arxiv.org/abs/2105.11087]
comment: Creating models that process and link information using various modalities.
subset: aio:NetworkSubset
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:MultimodalFusionLLM
name: Multimodal Fusion LLM
def: "A large language model that learns joint representations across different modalities like text vision and audio in an end-to-end fashion for better cross-modal understanding and generation." []
subset: aio:ModelSubset
synonym: "cross-modal grounding" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:MultimodalLLM
name: Multimodal LLM
def: "A multimodal large language model that learns joint representations across different modalities like text vision and audio in an end-to-end fashion for better cross-modal understanding and generation." [https://arxiv.org/abs/2303.17580]
subset: aio:ModelSubset
synonym: "cross-modal grounding" RELATED []
synonym: "Multimodal Large Language Model" EXACT []
is_a: aio:MultimodalLanguageModel ! Multimodal Language Model

[Term]
id: aio:MultimodalLanguageModel
name: Multimodal Language Model
def: "A language model that learns joint representations across different modalities like text vision and audio in an end-to-end fashion for better cross-modal understanding and generation." [https://arxiv.org/abs/2205.12630]
subset: aio:ModelSubset
synonym: "Mulimodal LM" EXACT []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:MultimodalLearning
name: Multimodal Learning
def: "A machine learning task that uses multiple modalities of data such as text audio and images to improve learning outcomes." [https://doi.org/10.6028/NIST.SP.1270]
comment: A type of deep learning that uses multiple modalities of data, such as text, audio, and images, to improve learning outcomes.
subset: aio:NetworkSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:MultimodalPromptbasedLanguageModel
name: Multimodal Prompt-based Language Model
def: "A multimodal large language model which processes prompts that include multiple modalities, such as both text and images, to generate relevant responses." [https://arxiv.org/abs/2210.03094]
subset: aio:ModelSubset
is_a: aio:MultimodalLanguageModel ! Multimodal Language Model

[Term]
id: aio:MultimodalTransformer
name: Multimodal Transformer
def: "A transformer network that processes and relates information from different modalities such as text images and audio using a shared embedding space and attention mechanism to learn joint representations across modalities." []
subset: aio:ModelSubset
synonym: "unified encoder" RELATED []
synonym: "vision-language model" RELATED []
is_a: aio:TransformerNetwork ! Transformer Network

[Term]
id: aio:MultiplyLayer
name: Multiply Layer
def: "A merging layer that multiplies (element-wise) a list of inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Multiply]
comment: Layer that multiplies (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
subset: aio:LayerSubset
is_a: aio:MergingLayer ! Merging Layer

[Term]
id: aio:NaturalLanguageProcessing
name: Natural Language Processing
def: "A machine learning task focused on the interactions between computers and human language including programming computers to process and analyze large amounts of natural language data." [https://en.wikipedia.org/wiki/Natural_language_processing]
subset: aio:MachineLearningSubset
synonym: "NLP" EXACT []
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:Network
name: Network
def: "A system of interconnected nodes or entities for communication, computation or data exchange." []
subset: aio:ClassSubset

[Term]
id: aio:NeuralTuringMachineNetwork
name: Neural Turing Machine Network
def: "A deep feedforward network that combines neural network pattern matching with the algorithmic power of programmable computers." [https://en.wikipedia.org/wiki/Neural_Turing_machine]
comment: Layers: Input, Hidden, Spiking Hidden, Output
subset: aio:NetworkSubset
synonym: "NTM" EXACT []
is_a: aio:DeepFeedForwardNetwork ! Deep Feed-Forward Network
relationship: BFO:0000051 aio:SpikingHiddenLayer ! has part Spiking Hidden Layer

[Term]
id: aio:NeuroSymbolicLLM
name: Neuro-Symbolic LLM
def: "A large language model which combines neural language modeling with symbolic reasoning components, leveraging structured knowledge representations and logical inferences to improve reasoning capabilities." []
subset: aio:ModelSubset
synonym: "knowledge reasoning" RELATED []
synonym: "Neuro-Symbolic Large Language Model" EXACT []
synonym: "symbolic grounding" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:NoiseDenseLayer
name: Noise Dense Layer
def: "A layer that is a densely-connected neural network layer with added noise for regularization." [https://www.tensorflow.org/addons/api_docs/python/tfa/layers/NoisyDense]
comment: Noisy dense layer that injects random noise to the weights of dense layer. Noisy dense layers are fully connected layers whose weights and biases are augmented by factorised Gaussian noise. The factorised Gaussian noise is controlled through gradient descent by a second weights layer. A NoisyDense layer implements the operation: $$ mathrm\{NoisyDense\}(x) = mathrm\{activation\}(mathrm\{dot\}(x, mu + (sigma cdot epsilon)) mathrm\{bias\}) $$ where mu is the standard weights layer, epsilon is the factorised Gaussian noise, and delta is a second weights layer which controls epsilon.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:NoisyInputLayer
name: Noisy Input Layer
def: "An input layer that adds noise to each value." [https://doi.org/10.1109/21.155944]
subset: aio:LayerSubset
is_a: aio:InputLayer ! Input Layer

[Term]
id: aio:Normalization
name: Normalization
def: "A data preparation that transforms data into a standard format or scale typically to reduce redundancy and improve consistency often involving the adjustment of values measured on different scales to a common scale." []
subset: aio:PreprocessingSubset
is_a: aio:DataPreparation ! Data Preparation

[Term]
id: aio:NormalizationLayer
name: Normalization Layer
def: "A numerical features prepreprocessing layer that normalizes continuous features." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Normalization]
subset: aio:LayerSubset
is_a: aio:NumericalFeaturesPreprocessingLayer ! Numerical Features Preprocessing Layer

[Term]
id: aio:NumericalFeaturesPreprocessingLayer
name: Numerical Features Preprocessing Layer
def: "A layer that performs numerical data preprocessing operations." [https://keras.io/guides/preprocessing_layers/]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:OneshotLearning
name: One-shot Learning
def: "A deep neural network that classified objects from one or only a few examples." [https://en.wikipedia.org/wiki/One-shot_learning]
subset: aio:NetworkSubset
synonym: "OSL" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:OrdinalLLM
name: Ordinal LLM
def: "A large language model that is trained to model ordinal relationships and rank outputs rather than model probability distributions over text sequences directly." []
subset: aio:ModelSubset
synonym: "Ordinal Large Language Model" EXACT []
synonym: "preference modeling" RELATED []
synonym: "ranking" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:OutputLayer
name: Output Layer
def: "A layer containing the last neurons in the network that produces given outputs for the program." [https://www.techopedia.com/definition/33263/output-layer-neural-networks]
comment: The output layer in an artificial neural network is the last layer of neurons that produces given outputs for the program. Though they are made much like other artificial neurons in the neural network, output layer neurons may be built or observed in a different way, given that they are the last “actor” nodes on the network.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:PReLULayer
name: PReLU Layer
def: "An activation layer that applies parametric rectified linear unit function element-wise." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/PReLU]
comment: Parametric Rectified Linear Unit.
subset: aio:LayerSubset
is_a: aio:ActivationLayer ! Activation Layer

[Term]
id: aio:Perceptron
name: Perceptron
def: "An artificial neural network with a supervised learning algorithm for binary classification using a linear predictor function." []
comment: Layers: Input, Output
subset: aio:NetworkSubset
synonym: "Feed-Forward Network" EXACT []
synonym: "FFN" EXACT []
synonym: "Single Layer Perceptron" EXACT []
synonym: "SLP" EXACT []
is_a: aio:ArtificialNeuralNetwork ! Artificial Neural Network
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:PermuteLayer
name: Permute Layer
def: "A reshaping layer that permutes the dimensions of the input according to a given pattern." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Permute]
comment: Permutes the dimensions of the input according to a given pattern. Useful e.g. connecting RNNs and convnets.
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:PersonalizedLLM
name: Personalized LLM
def: "A large language model that adapts its language modeling and generation to the preferences style and persona of individual users or audiences." []
subset: aio:ModelSubset
synonym: "Personalized Large Language Model" EXACT []
synonym: "user adaptation LLM" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:PolicyLayer
name: Policy Layer
def: "A layer that, after taking a set of states or values as input, predicts a probability distribution of actions to take." []
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:PoolingLayer
name: Pooling Layer
def: "A layer that serves to mitigate the sensitivity of convolutional layers to location and spatially downsample representations." [https://d2l.ai/chapter_convolutional-neural-networks/pooling.html]
comment: Pooling layers serve the dual purposes of mitigating the sensitivity of convolutional layers to location and of spatially downsampling representations.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:PopularityBias
name: Popularity Bias
def: "A selection and sampling bias where more popular items are more exposed under-representing less popular items." [https://doi.org/10.6028/NIST.SP.1270]
comment: Selection bias where more popular items are more exposed, under-representing less popular items.
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:PopulationBias
name: Population Bias
def: "A selection and sampling bias characterized by systematic distortions in demographics or other user characteristics between represented users and the target population." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:Preprocessing
name: Preprocessing
def: "A process applied to raw data before it is used in a machine learning model, including tasks such as normalization, scaling, encoding, and transformation, to ensure the data is in an appropriate format and quality for analysis." [https://doi.org/10.1109/ICDE.2019.00245]
subset: aio:PreprocessingSubset

[Term]
id: aio:PreprocessingLayer
name: Preprocessing Layer
def: "A layer that performs data preprocessing operations." [https://www.tensorflow.org/guide/keras/preprocessing_layers]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:PresentationBias
name: Presentation Bias
def: "An individual bias arising from how information is presented on the Web via a user interface due to rating or ranking of output or through users' self-selected biased interaction." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:PrincipalComponentAnalysis
name: Principal Component Analysis
def: "A dimensionality reduction method for analyzing large datasets with high-dimensional features per observation increasing data interpretability while preserving maximum information and enabling visualization." [https://en.wikipedia.org/wiki/Principal_component_analysis]
subset: aio:MachineLearningSubset
synonym: "PCA" EXACT []
is_a: aio:DimensionalityReduction ! Dimensionality Reduction

[Term]
id: aio:ProbabilisticGraphicalModel
name: Probabilistic Graphical Model
def: "A machine learning task in which a graph expresses the conditional dependence structure between random variables." [https://en.wikipedia.org/wiki/Graphical_model]
subset: aio:MachineLearningSubset
synonym: "Graphical Model" EXACT []
synonym: "PGM" EXACT []
synonym: "Structure Probabilistic Model" EXACT []
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:ProbabilisticHiddenLayer
name: Probabilistic Hidden Layer
def: "A hidden layer that estimates the probability of a sample being within a certain category." []
subset: aio:LayerSubset
is_a: aio:HiddenLayer ! Hidden Layer

[Term]
id: aio:ProbabilisticTopicModel
name: Probabilistic Topic Model
def: "A probabilistic graphical model that uses statistical techniques to analyze the words in each text to discover common themes their connections and their changes over time." [https://pyro.ai/examples/prodlda.html]
subset: aio:MachineLearningSubset
is_a: aio:ProbabilisticGraphicalModel ! Probabilistic Graphical Model

[Term]
id: aio:ProcessingBias
name: Processing Bias
def: "A computational bias resulting from judgment modulated by affect influenced by the level of efficacy and efficiency in information processing." [https://en.wikipedia.org/wiki/Bias_(statistics)]
comment: Judgment modulated by affect, influenced by the level of efficacy and efficiency in information processing; often referred to as aesthetic judgment in cognitive sciences.
subset: aio:BiasSubset
synonym: "Validation Bias" EXACT []
is_a: aio:ComputationalBias ! Computational Bias

[Term]
id: aio:PromptbasedFineTuningLLM
name: Prompt-based Fine-Tuning LLM
def: "A large language model which is fine-tuned on a small number of examples or prompts, rather than full task datasets. This allows for rapid adaptation to new tasks with limited data, leveraging the model's few-shot learning capabilities." []
subset: aio:ModelSubset
synonym: "few-shot learning" RELATED []
synonym: "in-context learning" RELATED []
synonym: "Prompt-based Fine-Tuning Large Language Model" EXACT []
synonym: "Prompt-tuned Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ProportionalHazardsModel
name: Proportional Hazards Model
def: "A regression analysis for survival analysis where the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate." [https://en.wikipedia.org/wiki/Proportional_hazards_model]
subset: aio:MachineLearningSubset
is_a: aio:SurvivalAnalysis ! Survival Analysis

[Term]
id: aio:RNNLayer
name: RNN Layer
def: "The base class for recurrent layers." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RNN]
comment: Base class for recurrent layers.
subset: aio:LayerSubset
is_a: aio:RecurrentLayer ! Recurrent Layer

[Term]
id: aio:RadialBasisNetwork
name: Radial Basis Network
def: "A deep feedforward network that uses radial basis functions as activation functions for pattern recognition and interpolation." [https://en.wikipedia.org/wiki/Radial_basis_function_network]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "Radial Basis Function Network" EXACT []
synonym: "RBFN" EXACT []
synonym: "RBN" EXACT []
is_a: aio:DeepFeedForwardNetwork ! Deep Feed-Forward Network

[Term]
id: aio:RandomBrightnessLayer
name: RandomBrightness Layer
def: "An image preprocessing layer that randomly adjusts brightness during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomBrightness]
comment: A preprocessing layer which randomly adjusts brightness during training. This layer will randomly increase/reduce the brightness for the input RGB images. At inference time, the output will be identical to the input. Call the layer with training=True to adjust the brightness of the input. Note that different brightness adjustment factors will be apply to each the images in the batch.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RandomContrastLayer
name: RandomContrast Layer
def: "An image preprocessing layer that randomly adjusts contrast during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomContrast]
comment: A preprocessing layer which randomly adjusts contrast during training. This layer will randomly adjust the contrast of an image or images by a random factor. Contrast is adjusted independently for each channel of each image during training. For each channel, this layer computes the mean of the image pixels in the channel and then adjusts each component x of each pixel to (x - mean) * contrast_factor + mean. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and in integer or floating point dtype. By default, the layer will output floats. The output value will be clipped to the range [0, 255], the valid range of RGB colors.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RandomCropLayer
name: RandomCrop Layer
def: "An image preprocessing layer that randomly crops images during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomCrop]
comment: A preprocessing layer which randomly crops images during training. During training, this layer will randomly choose a location to crop images down to a target size. The layer will crop all the images in the same batch to the same cropping location. At inference time, and during training if an input image is smaller than the target size, the input will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. If you need to apply random cropping at inference time, set training to True when calling the layer. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RandomEffectsModel
name: Random Effects Model
def: "A regression analysis where the model parameters are random variables." [https://en.wikipedia.org/wiki/Random_effects_model]
subset: aio:MachineLearningSubset
synonym: "REM" EXACT []
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:RandomFlipLayer
name: RandomFlip Layer
def: "An image preprocessing layer that randomly flips images during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomFlip]
comment: A preprocessing layer which randomly flips images during training. This layer will flip the images horizontally and or vertically based on the mode attribute. During inference time, the output will be identical to input. Call the layer with training=True to flip the input. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RandomForest
name: Random Forest
def: "An ensemble learning method for classification regression and other tasks that constructs a multitude of decision trees during training." [https://en.wikipedia.org/wiki/Random_forest]
subset: aio:MachineLearningSubset
is_a: aio:EnsembleLearning ! Ensemble Learning

[Term]
id: aio:RandomHeightLayer
name: RandomHeight Layer
def: "An image preprocessing layer that randomly varies image height during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomHeight]
comment: A preprocessing layer which randomly varies image height during training. This layer adjusts the height of a batch of images by a random factor. The input should be a 3D (unbatched) or 4D (batched) tensor in the "channels_last" image data format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. By default, this layer is inactive during inference.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RandomRotationLayer
name: RandomRotation Layer
def: "An image preprocessing layer that randomly rotates images during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomRotation]
comment: A preprocessing layer which randomly rotates images during training.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RandomTranslationLayer
name: RandomTranslation Layer
def: "An image preprocessing layer that randomly translates images during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomTranslation]
comment: A preprocessing layer which randomly translates images during training. This layer will apply random translations to each image during training, filling empty space according to fill_mode. aInput pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RandomWidthLayer
name: RandomWidth Layer
def: "An image preprocessing layer that randomly varies image width during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomWidth]
comment: A preprocessing layer which randomly varies image width during training. This layer will randomly adjusts the width of a batch of images of a batch of images by a random factor. The input should be a 3D (unbatched) or 4D (batched) tensor in the "channels_last" image data format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. By default, this layer is inactive during inference.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RandomZoomLayer
name: RandomZoom Layer
def: "An image preprocessing layer that randomly zooms in or out on images during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomZoom]
comment: A preprocessing layer which randomly zooms images during training. This layer will randomly zoom in or out on each axis of an image independently, filling empty space according to fill_mode.Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RankingBias
name: Ranking Bias
def: "An anchoring bias characterized by the idea that top-ranked results are the most relevant and important leading to more clicks than other results." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:AnchoringBias ! Anchoring Bias

[Term]
id: aio:RashomonEffectBias
name: Rashomon Effect Bias
def: "An individual bias characterized by differences in perspective memory recall interpretation and reporting of the same event by multiple persons or witnesses." [https://doi.org/10.6028/NIST.SP.1270]
comment: Differences in perspective, memory, recall, interpretation, and reporting of the same event by multiple persons or witnesses.
subset: aio:BiasSubset
synonym: "Rashomon Effect" EXACT []
synonym: "Rashomon Principle" EXACT []
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:ReLUFunction
name: ReLU Function
def: "A mathematical function that returns max(x 0) the element-wise maximum of 0 and the input tensor." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu]
comment: The ReLU activation function returns: max(x, 0), the element-wise maximum of 0 and the input tensor.
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
synonym: "Rectified Linear Unit" EXACT []
synonym: "ReLU" EXACT []
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:ReLULayer
name: ReLU Layer
def: "An activation layer that applies the rectified linear unit function element-wise." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ReLU]
comment: Rectified Linear Unit activation function. With default values, it returns element-wise max(x, 0).
subset: aio:LayerSubset
is_a: aio:ActivationLayer ! Activation Layer

[Term]
id: aio:ReasoningLLM
name: Reasoning LLM
def: "A large language model that incorporates explicit reasoning capabilities leveraging logical rules axioms or external knowledge to make deductive inferences during language tasks." [https://doi.org/10.18653/v1/2023.acl-long.347]
subset: aio:ModelSubset
synonym: "logical inferences" RELATED []
synonym: "Rational Large Language Model" EXACT []
synonym: "reasoning" RELATED []
synonym: "Reasoning Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:RecurrentLayer
name: Recurrent Layer
def: "A layer composed of recurrent units with the number equal to the hidden size of the layer." [https://docs.nvidia.com/deepLearning/performance/dl-performance-recurrent/index.html#recurrent-layer]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:RecurrentNeuralNetwork
name: Recurrent Neural Network
def: "A network with connections forming a directed graph along a temporal sequence enabling dynamic behavior." []
subset: aio:NetworkSubset
synonym: "RecNN" EXACT []
synonym: "Recurrent Network" EXACT []
synonym: "RN" EXACT []
is_a: aio:Network ! Network

[Term]
id: aio:RecursiveLLM
name: Recursive LLM
def: "A language model that uses recursive neural network architectures like TreeLSTMs to learn syntactic composition functions improving systematic generalization abilities." [https://doi.org/10.1609/aaai.v33i01.33017450]
subset: aio:ModelSubset
synonym: "iterative refinement" RELATED []
synonym: "Recursive Large Language Model" EXACT []
synonym: "Self-Attending Large Language Model" EXACT []
synonym: "self-attention" RELATED []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:RecursiveLanguageModel
name: Recursive Language Model
def: "A deep neural network that uses recursive neural network architectures like TreeLSTMs to learn syntactic composition functions improving systematic generalization abilities." [https://en.wikipedia.org/wiki/Recurrent_neural_network]
comment: Layers: Input, Memory Cell, Output
subset: aio:ModelSubset
synonym: "Compositional generalization" RELATED []
synonym: "RLM" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:MemoryCellLayer ! has part Memory Cell Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:RecursiveNeuralNetwork
name: Recursive Neural Network
def: "A deep neural network that recursively applies weights over structured input to generate structured or scalar predictions." [https://en.wikipedia.org/wiki/Recursive_neural_network]
subset: aio:NetworkSubset
synonym: "RecuNN" EXACT []
synonym: "RvNN" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:RegressionAnalysis
name: Regression Analysis
def: "A set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables." [https://en.wikipedia.org/wiki/Regression_analysis]
subset: aio:MachineLearningSubset
synonym: "Regression analysis" EXACT []
synonym: "Regression model" EXACT []
is_a: aio:SupervisedLearning ! Supervised Learning

[Term]
id: aio:RegularizationLayer
name: Regularization Layer
def: "A layer that applies penalties on layer parameters or layer activity during optimization summed into the loss function that the network optimizes." [https://keras.io/api/layers/regularizers/]
comment: Regularizers allow you to apply penalties on layer parameters or layer activity during optimization. These penalties are summed into the loss function that the network optimizes. Regularization penalties are applied on a per-layer basis.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:ReinforcementLearning
name: Reinforcement Learning
def: "A machine learning task focused on methods that do not require labeled input/output pairs or explicit correction of sub-optimal actions focusing instead on balancing exploration and exploitation to optimize performance over time." [https://en.wikipedia.org/wiki/Reinforcement_learning]
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:ReinforcementLearningLLM
name: Reinforcement Learning LLM
def: "A large language model that is fine-tuned using reinforcement learning where the model receives rewards for generating text that satisfies certain desired properties or objectives improving the quality safety or alignment of generated text." []
comment: An RL-LLM is a language model fine-tuned using reinforcement learning, where the model receives rewards for generating text that satisfies certain desired properties or objectives. This can improve the quality, safety, or alignment of generated text.
subset: aio:ModelSubset
synonym: "decision transformers" RELATED []
synonym: "Reinforcement Learning Large Language Model" EXACT []
synonym: "reward modeling" RELATED []
synonym: "RL-Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:RepeatVectorLayer
name: RepeatVector Layer
def: "A reshaping layer that repeats the input n times." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RepeatVector]
comment: Repeats the input n times.
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:RepresentationBias
name: Representation Bias
def: "A selection and sampling bias due to non-random sampling of subgroups making trends non-generalizable to new populations." [https://doi.org/10.6028/NIST.SP.1270]
comment: Bias due to non-random sampling of subgroups, making trends non-generalizable to new populations.
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:RepresentationLearning
name: Representation Learning
def: "A deep neural network that discovers representations required for feature detection or classification from raw data." [https://en.wikipedia.org/wiki/Feature_learning]
comment: Discovering representations required for feature detection or classification from raw data.
subset: aio:NetworkSubset
synonym: "Feature Learning" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:RescalingLayer
name: Rescaling Layer
def: "An image preprocessing layer that rescales input values to a new range." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Rescaling]
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:ReshapeLayer
name: Reshape Layer
def: "A reshaping layer that reshapes the inputs into the given shape." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Reshape]
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:ReshapingLayer
name: Reshaping Layer
def: "A layer that is used to change the shape of the input." [https://keras.io/api/layers/reshaping_layers/reshape/]
comment: Reshape layers are used to change the shape of the input.
subset: aio:LayerSubset
synonym: "Reshape Layer" EXACT []
is_a: aio:Layer ! Layer

[Term]
id: aio:ResidualNeuralNetwork
name: Residual Neural Network
def: "A deep neural network that employs skip connections to bypass layers facilitating learning of residual functions." [https://en.wikipedia.org/wiki/Residual_neural_network]
comment: Layers: Input, Weight, BN, ReLU, Weight, BN, Addition, ReLU
subset: aio:NetworkSubset
synonym: "Deep Residual Network" EXACT []
synonym: "DRN" EXACT []
synonym: "ResNet" EXACT []
synonym: "ResNN" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network
relationship: BFO:0000051 aio:AdditionLayer ! has part Addition Layer
relationship: BFO:0000051 aio:BatchNormalizationLayer ! has part BatchNormalization Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:ReLULayer ! has part ReLU Layer
relationship: BFO:0000051 aio:WeightedLayer ! has part Weighted Layer

[Term]
id: aio:ResizingLayer
name: Resizing Layer
def: "An image preprocessing layer that resizes images to a target size." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Resizing]
comment: A preprocessing layer which resizes images. This layer resizes an image input to a target height and width. The input should be a 4D (batched) or 3D (unbatched) tensor in "channels_last" format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. This layer can be called on tf.RaggedTensor batches of input images of distinct sizes, and will resize the outputs to dense tensors of uniform size.
subset: aio:LayerSubset
is_a: aio:ImagePreprocessingLayer ! Image Preprocessing Layer

[Term]
id: aio:RestrictedBoltzmannMachine
name: Restricted Boltzmann Machine
def: "A Boltzmann machine network that learns the probability distribution of its input data." [https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine]
comment: Layers: Backfed Input, Probabilistic Hidden
subset: aio:NetworkSubset
synonym: "RBM" EXACT []
is_a: aio:BoltzmannMachineNetwork ! Boltzmann Machine Network

[Term]
id: aio:RetrievalAugmentedLLM
name: Retrieval-Augmented LLM
def: "A large language model which combines a pre-trained language model with a retrieval system that can access external knowledge sources. This allows the model to condition its generation on relevant retrieved knowledge, improving factual accuracy and knowledge grounding." []
subset: aio:ModelSubset
synonym: "knowledge grounding" RELATED []
synonym: "open-book question answering" RELATED []
synonym: "Retrieval-Augmented Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:RidgeRegression
name: Ridge Regression
def: "A regression analysis that estimates the coefficients of multiple regression models in scenarios where the independent variables are highly correlated." [https://en.wikipedia.org/wiki/Ridge_regression]
subset: aio:MachineLearningSubset
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:SELUFunction
name: SELU Function
def: "A mathematical function that multiplies scale (> 1) with the output of the ELU function to ensure a slope larger than one for positive inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/selu]
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
synonym: "Scaled Exponential Linear Unit" EXACT []
synonym: "SELU" EXACT []
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:SeasonalAutoregressiveIntegratedMovingAverage
name: Seasonal Autoregressive Integrated Moving-Average
def: "A model that extends ARIMA, explicitly supporting univariate time series data with a seasonal component, combining seasonal differencing with ARIMA modeling." []
subset: aio:ModelSubset
synonym: "SARIMA" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:SelectionAndSamplingBias
name: Selection And Sampling Bias
def: "A computational bias introduced by non-random selection of individuals groups or data failing to ensure representativeness." [https://doi.org/10.6028/NIST.SP.1270]
comment: Bias introduced by non-random selection of individuals, groups, or data, failing to ensure representativeness.
subset: aio:BiasSubset
synonym: "Sampling Bias" EXACT []
synonym: "Selection Bias" EXACT []
synonym: "Selection Effect" EXACT []
is_a: aio:ComputationalBias ! Computational Bias

[Term]
id: aio:SelectiveAdherenceBias
name: Selective Adherence Bias
def: "An individual bias characterized by the tendency to selectively adopt algorithmic advice that matches pre-existing beliefs and stereotypes." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:SelfSupervisedLLM
name: Self-Supervised LLM
def: "A large language model which learns rich representations by solving pretext tasks that involve predicting parts of the input from other observed parts of the data, without relying on human-annotated labels." []
subset: aio:ModelSubset
synonym: "Pretext tasks" RELATED []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:SelfsupervisedLearning
name: Self-supervised Learning
def: "A machine learning task that is intermediate between supervised and unsupervised learning and predicts parts of the input data from other observed parts without relying on human-annotated labels." [https://en.wikipedia.org/wiki/Self-supervised_learning]
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:SemiSupervisedLLM
name: Semi-Supervised LLM
def: "A large language model which combines self-supervised pretraining on unlabeled data with supervised fine-tuning on labeled task data." []
subset: aio:ModelSubset
synonym: "self-training" RELATED []
synonym: "Semi-Supervised Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:SeparableConvolution1DLayer
name: SeparableConvolution1D Layer
def: "A convolutional layer that performs depthwise separable 1D convolution." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SeparableConv1D]
comment: Depthwise separable 1D convolution. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If use_bias is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.a
subset: aio:LayerSubset
synonym: "SeparableConv1D Layer" EXACT []
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:SeparableConvolution2DLayer
name: SeparableConvolution2D Layer
def: "A convolutional layer that performs depthwise separable 2D convolution." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SeparableConv2D]
comment: Depthwise separable 2D convolution. Separable convolutions consist of first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes the resulting output channels. The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step. Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.
subset: aio:LayerSubset
synonym: "SeparableConv2D Layer" EXACT []
is_a: aio:ConvolutionalLayer ! Convolutional Layer

[Term]
id: aio:SigmoidFunction
name: Sigmoid Function
def: "A mathematical function that applies the sigmoid activation function sigmoid(x) = 1 / (1 + exp(-x)) always returning a value between 0 and 1." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid]
comment: Applies the sigmoid activation function sigmoid(x) = 1 / (1 + exp(-x)). For small values (<-5), sigmoid returns a value close to zero, and for large values (>5) the result of the function gets close to 1. Sigmoid is equivalent to a 2-element Softmax, where the second element is assumed to be zero. The sigmoid function always returns a value between 0 and 1.
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
synonym: "tore" EXACT []
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:SimpleRNNCellLayer
name: SimpleRNNCell Layer
def: "A layer that processes one step within the whole time sequence input for a SimpleRNN layer." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SimpleRNNCell]
comment: Cell class for SimpleRNN. This class processes one step within the whole time sequence input, whereas tf.keras.layer.SimpleRNN processes the whole sequence.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:SimpleRNNLayer
name: SimpleRNN Layer
def: "A recurrent layer that implements a fully-connected RNN where the output is to be fed back to input." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SimpleRNN]
comment: Fully-connected RNN where the output is to be fed back to input.
subset: aio:LayerSubset
is_a: aio:RecurrentLayer ! Recurrent Layer

[Term]
id: aio:SimponsParadoxBias
name: Simpon's Paradox Bias
def: "A selection and sampling bias where the association between two variables changes when controlling for another variable." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Simpson's Paradox" EXACT []
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:SocietalBias
name: Societal Bias
def: "A systemic bias characterized by being for or against groups or individuals based on social identities demographic factors or immutable physical characteristics often manifesting as stereotypes." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:SystemicBias ! Systemic Bias

[Term]
id: aio:SoftmaxFunction
name: Softmax Function
def: "A mathematical function where the elements of the output vector are in range (0 1) and sum to 1 and each vector is handled independently." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax]
comment: The elements of the output vector are in range (0, 1) and sum to 1. Each vector is handled independently. The axis argument sets which axis of the input the function is applied along. Softmax is often used as the activation for the last layer of a classification network because the result could be interpreted as a probability distribution. The softmax of each vector x is computed as exp(x) / tf.reduce_sum(exp(x)). The input values in are the log-odds of the resulting probability.
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:SoftmaxLayer
name: Softmax Layer
def: "An activation layer that applies the softmax function to the inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Softmax]
comment: Softmax activation function.
subset: aio:LayerSubset
is_a: aio:ActivationLayer ! Activation Layer

[Term]
id: aio:SoftplusFunction
name: Softplus Function
def: "A mathematical function that is softplus(x) = log(exp(x) + 1)." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/softplus]
comment: softplus(x) = log(exp(x) + 1)
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:SoftsignFunction
name: Softsign Function
def: "A mathematical function that is softsign(x) = x / (abs(x) + 1)." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/softsign]
comment: softsign(x) = x / (abs(x) + 1)
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:SparseAutoEncoder
name: Sparse Auto Encoder
def: "An autoencoder network with more hidden units than inputs that constrains only a few hidden units to be active at once." []
comment: Layers: Input, Hidden, Matched Output-Input
subset: aio:NetworkSubset
synonym: "SAE" EXACT []
synonym: "Sparse AE" EXACT []
synonym: "Sparse Autoencoder" EXACT []
is_a: aio:AutoEncoderNetwork ! Auto Encoder Network

[Term]
id: aio:SparseLLM
name: Sparse LLM
def: "A large language model that uses techniques like pruning or quantization to reduce the number of non-zero parameters in the model making it more parameter-efficient and easier to deploy on resource-constrained devices." []
subset: aio:ModelSubset
synonym: "model compression" RELATED []
synonym: "parameter efficiency" RELATED []
synonym: "Sparse Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:SparseLearning
name: Sparse Learning
def: "A representation learning network that finds sparse representations of input data as a linear combination of basic elements and identifies those elements." [https://en.wikipedia.org/wiki/Sparse_dictionary_learning]
subset: aio:NetworkSubset
synonym: "Sparse coding" EXACT []
synonym: "Sparse dictionary Learning" EXACT []
is_a: aio:RepresentationLearning ! Representation Learning

[Term]
id: aio:SpatialDropout1DLayer
name: SpatialDropout1D Layer
def: "A regularization layer that performs the same function as Dropout but drops entire 1D feature maps instead of individual elements." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout1D]
comment: Spatial 1D version of Dropout. This version performs the same function as Dropout, however, it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout1D will help promote independence between feature maps and should be used instead.
subset: aio:LayerSubset
is_a: aio:RegularizationLayer ! Regularization Layer

[Term]
id: aio:SpatialDropout2DLayer
name: SpatialDropout2D Layer
def: "A regularization layer that performs the same function as Dropout but drops entire 2D feature maps instead of individual elements." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout2D]
comment: Spatial 2D version of Dropout. This version performs the same function as Dropout, however, it drops entire 2D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout2D will help promote independence between feature maps and should be used instead.a
subset: aio:LayerSubset
is_a: aio:RegularizationLayer ! Regularization Layer

[Term]
id: aio:SpatialDropout3DLayer
name: SpatialDropout3D Layer
def: "A regularization layer that performs the same function as Dropout but drops entire 3D feature maps instead of individual elements." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout3D]
comment: Spatial 3D version of Dropout. This version performs the same function as Dropout, however, it drops entire 3D feature maps instead of individual elements. If adjacent voxels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout3D will help promote independence between feature maps and should be used instead.
subset: aio:LayerSubset
is_a: aio:RegularizationLayer ! Regularization Layer

[Term]
id: aio:SpatialRegression
name: Spatial Regression
def: "A regression analysis used to model spatial relationships." [https://gisgeography.com/spatial-regression-models-arcgis/]
subset: aio:MachineLearningSubset
is_a: aio:RegressionAnalysis ! Regression Analysis

[Term]
id: aio:SpikingHiddenLayer
name: Spiking Hidden Layer
def: "A hidden layer that makes connections to an additional, heterogeneous hidden layer; modeled after biological neural networks." [https://doi.org/10.1016/S0893-6080(97)00011-7]
subset: aio:LayerSubset
is_a: aio:HiddenLayer ! Hidden Layer

[Term]
id: aio:StackedRNNCellsLayer
name: StackedRNNCells Layer
def: "A layer that allows a stack of RNN cells to behave as a single cell." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/StackedRNNCells]
comment: Wrapper allowing a stack of RNN cells to behave as a single cell. Used to implement efficient stacked RNNs.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:StreetlightEffectBias
name: Streetlight Effect Bias
def: "An individual bias where people search only where it is easiest to look." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
synonym: "Streetlight Effect" EXACT []
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:StringLookupLayer
name: StringLookup Layer
def: "A categorical features preprocessing layer that maps string features to integer indices." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/StringLookup]
subset: aio:LayerSubset
is_a: aio:CategoricalFeaturesPreprocessingLayer ! Categorical Features Preprocessing Layer

[Term]
id: aio:SubtractLayer
name: Subtract Layer
def: "A merging layer that subtracts two inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Subtract]
comment: Layer that subtracts two inputs. It takes as input a list of tensors of size 2, both of the same shape, and returns a single tensor, (inputs[0] - inputs[1]), also of the same shape.
subset: aio:LayerSubset
is_a: aio:MergingLayer ! Merging Layer

[Term]
id: aio:SubwordSegmentation
name: Subword Segmentation
def: "A data preparation that divides text into subword units which are smaller than words but larger than individual characters to improve the efficiency and effectiveness of natural language processing models by capturing meaningful subunits of words." []
subset: aio:PreprocessingSubset
synonym: "Byte Pair Encoding" RELATED []
synonym: "Fragmentation" EXACT []
synonym: "Part-word Division" EXACT []
synonym: "SentencePiece" RELATED []
is_a: aio:DataPreparation ! Data Preparation

[Term]
id: aio:SunkCostFallacyBias
name: Sunk Cost Fallacy Bias
def: "A bias characterized by the tendency to continue an endeavor due to previously invested resources despite costs outweighing benefits." [https://doi.org/10.6028/NIST.SP.1270]
comment: The tendency to continue an endeavor due to previously invested resources, despite costs outweighing benefits.
subset: aio:BiasSubset
synonym: "Sunk Cost Fallacy" EXACT []
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:SupervisedBiclustering
name: Supervised Biclustering
def: "A biclustering task focused on methods that simultaneously cluster the rows and columns of a labeled matrix considering data labels to enhance cluster coherence." [https://en.wikipedia.org/wiki/Biclustering]
subset: aio:MachineLearningSubset
synonym: "Supervised Block Clustering" EXACT []
synonym: "Supervised Co-clustering" EXACT []
synonym: "Supervised Joint Clustering" EXACT []
synonym: "Supervised Two-mode Clustering" EXACT []
synonym: "Supervised Two-way Clustering" EXACT []
is_a: aio:Biclustering ! Biclustering

[Term]
id: aio:SupervisedClustering
name: Supervised Clustering
def: "A clustering focused on methods that group labeled objects such that objects in the same group have similar labels relative to those in other groups." [https://en.wikipedia.org/wiki/Cluster_analysis]
subset: aio:MachineLearningSubset
synonym: "Supervised Cluster Analysis" EXACT []
is_a: aio:Clustering ! Clustering

[Term]
id: aio:SupervisedLearning
name: Supervised Learning
def: "A machine learning task focused on methods that learn a function mapping input to output based on example input-output pairs." [https://en.wikipedia.org/wiki/Supervised_learning]
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:SupportVectorMachine
name: Support Vector Machine
def: "A network with supervised learning models for classification and regression that maps training examples to points in space maximizing the gap between categories." [https://en.wikipedia.org/wiki/Support-vector_machine]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "Supper Vector Network" EXACT []
synonym: "SVM" EXACT []
synonym: "SVN" EXACT []
is_a: aio:Network ! Network
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:SurvivalAnalysis
name: Survival Analysis
def: "A machine learning task focused on methods for analyzing the expected duration of time until one or more events occur such as death in biological organisms or failure in mechanical systems." [https://en.wikipedia.org/wiki/Survival_analysis]
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:SurvivorshipBias
name: Survivorship Bias
def: "A processing bias characterized by the tendency to focus on items observations or people that \"survive\" a selection process overlooking those that did not." [https://doi.org/10.6028/NIST.SP.1270]
comment: The tendency to focus on items, observations, or people that "survive" a selection process, overlooking those that did not.
subset: aio:BiasSubset
is_a: aio:ProcessingBias ! Processing Bias

[Term]
id: aio:SwishFunction
name: Swish Function
def: "A mathematical function that is x*sigmoid(x) a smooth non-monotonic function that consistently matches or outperforms ReLU on deep networks." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/swish]
comment: x*sigmoid(x). It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below.
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:SymmetricallyConnectedNetwork
name: Symmetrically Connected Network
def: "A network that is a type of recurrent neural network where connections between units are symmetrical with equal weights in both directions." [https://ieeexplore.ieee.org/document/287176]
comment: Symmetrically connected networks are a type of recurrent neural network where connections between units are symmetrical, meaning they have equal weights in both directions. This structure allows the network to maintain consistent information flow and equilibrium.
subset: aio:NetworkSubset
synonym: "SCN" EXACT []
is_a: aio:Network ! Network

[Term]
id: aio:SyncBatchNormLayer
name: SyncBatchNorm Layer
def: "A batch normalization layer that applies synchronous Batch Normalization across multiple devices." [https://pytorch.org/docs/stable/nn.html#normalization-layers]
comment: Applies Batch Normalization over a N-Dimensional input (a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
subset: aio:LayerSubset
synonym: "SyncBatchNorm" EXACT []
is_a: aio:BatchNormalizationLayer ! BatchNormalization Layer

[Term]
id: aio:SystemicBias
name: Systemic Bias
def: "A bias resulting from procedures and practices of systems that operate in ways which result in certain social groups being advantaged or favored and others being disadvantaged or devalued." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:Bias ! Bias

[Term]
id: aio:TanhFunction
name: Tanh Function
def: "A mathematical function that is the hyperbolic tangent activation function." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/tanh]
comment: Hyperbolic tangent activation function.
subset: aio:ActivationFunctionSubset
subset: aio:FunctionSubset
synonym: "hyperbolic tangent" EXACT []
is_a: aio:MathematicalFunction ! Mathematical Function

[Term]
id: aio:TemporalBias
name: Temporal Bias
def: "A selection and sampling bias arising from differences in populations and behaviors over time." [https://doi.org/10.6028/NIST.SP.1270]
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:TextPreprocessingLayer
name: Text Preprocessing Layer
def: "A layer that performs text data preprocessing operations." [https://keras.io/guides/preprocessing_layers/]
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:TextVectorizationLayer
name: TextVectorization Layer
def: "A text preprocessing layer that maps text features to integer sequences." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization]
subset: aio:LayerSubset
is_a: aio:TextPreprocessingLayer ! Text Preprocessing Layer

[Term]
id: aio:ThresholdAutoregressive
name: Threshold Autoregressive
def: "A model that allows for different autoregressive processes depending on the regime or state of the time series, enabling the capture of nonlinear behaviors." [https://dx.doi.org/10.1080/01621459.1989.10478760]
subset: aio:ModelSubset
synonym: "TAR" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:ThresholdedReLULayer
name: ThresholdedReLU Layer
def: "An activation layer that applies the thresholded rectified linear unit function element-wise." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ThresholdedReLU]
comment: Thresholded Rectified Linear Unit.
subset: aio:LayerSubset
is_a: aio:ActivationLayer ! Activation Layer

[Term]
id: aio:TimeDistributedLayer
name: TimeDistributed Layer
def: "A recurrent layer that applies a layer to every temporal slice of an input." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/TimeDistributed]
comment: This wrapper allows to apply a layer to every temporal slice of an input. Every input should be at least 3D, and the dimension of index one of the first input will be considered to be the temporal dimension. Consider a batch of 32 video samples, where each sample is a 128x128 RGB image with channels_last data format, across 10 timesteps. The batch input shape is (32, 10, 128, 128, 3). You can then use TimeDistributed to apply the same Conv2D layer to each of the 10 timesteps, independently:
subset: aio:LayerSubset
is_a: aio:RecurrentLayer ! Recurrent Layer

[Term]
id: aio:TimeSeriesAnalysis
name: Time Series Analysis
def: "A machine learning task focused on methods for analyzing time series data to extract meaningful statistics and characteristics." [https://en.wikipedia.org/wiki/Time_series]
comment: Methods for analyzing time series data to extract meaningful statistics and characteristics.
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:TimeSeriesForecasting
name: Time Series Forecasting
def: "A machine learning task focused on methods that predict future values based on previously observed values." [https://en.wikipedia.org/wiki/Time_series]
comment: Methods that predict future values based on previously observed values.
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:Tokenization
name: Tokenization
def: "A data preparation that converts a sequence of text into smaller meaningful units called tokens typically words or subwords for the purpose of analysis or processing by language models." []
subset: aio:PreprocessingSubset
synonym: "Lexical Analysis" EXACT []
synonym: "Text Segmentation" EXACT []
is_a: aio:DataPreparation ! Data Preparation

[Term]
id: aio:TrainingStrategy
name: TrainingStrategy
def: "A preprocessing used to train machine learning models including techniques such as supervised learning unsupervised learning reinforcement learning and transfer learning aimed at optimizing model performance." []
subset: aio:TrainingStrategySubset
synonym: "Instructional Methods" EXACT []
synonym: "Learning Techniques" EXACT []

[Term]
id: aio:TransferLearning
name: Transfer Learning
def: "A machine learning task focused on methods that reuse or transfer information from previously learned tasks to facilitate the learning of new tasks." [https://en.wikipedia.org/wiki/Transfer_learning]
subset: aio:NetworkSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:TransferLearningLLM
name: Transfer Learning LLM
def: "A large language model that leverages knowledge acquired during training on one task to improve performance on different but related tasks facilitating more efficient learning and adaptation." []
subset: aio:ModelSubset
synonym: "transfer learning" RELATED []
synonym: "Transfer LLM" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:TransformerLLM
name: Transformer LLM
def: "A transformer language model with large training corpuses and sets of parameters that uses the transformer architecture based on multi-head attention mechanisms allowing it to contextualize tokens within a context window for effective language understanding and generation." [https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)]
subset: aio:ModelSubset
synonym: "Transformer Large Language Model" EXACT []
is_a: aio:TransformerLanguageModel ! Transformer Language Model

[Term]
id: aio:TransformerLanguageModel
name: Transformer Language Model
def: "A language model that uses the transformer architecture based on multi-head attention mechanisms allowing it to contextualize tokens within a context window for effective language understanding and generation." [https://arxiv.org/abs/1706.03762]
subset: aio:ModelSubset
synonym: "Transformer LM" EXACT []
is_a: aio:LanguageModel ! Language Model

[Term]
id: aio:TransformerNetwork
name: Transformer Network
def: "A deep neural network that utilizes attention mechanisms to weigh the significance of input data." [https://en.wikipedia.org/wiki/Transformer_(machine_Learning_model)]
comment: A transformer network utilizes attention mechanisms to weigh the significance of each part of the input data, widely used in natural language processing (NLP) and computer vision (CV).
subset: aio:NetworkSubset
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:UncertaintyBias
name: Uncertainty Bias
def: "A selection and sampling bias favoring groups better represented in training data due to less prediction uncertainty." [https://doi.org/10.6028/NIST.SP.1270]
comment: Bias favoring groups better represented in training data, due to less prediction uncertainty.
subset: aio:BiasSubset
is_a: aio:SelectionAndSamplingBias ! Selection And Sampling Bias

[Term]
id: aio:UnitNormalizationLayer
name: UnitNormalization Layer
def: "A recurrent layer that normalizes a batch of inputs so that each input in the batch has a L2 norm equal to 1." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/UnitNormalization]
comment: Unit normalization layer. Normalize a batch of inputs so that each input in the batch has a L2 norm equal to 1 (across the axes specified in axis).
subset: aio:LayerSubset
is_a: aio:RecurrentLayer ! Recurrent Layer

[Term]
id: aio:UnsupervisedBiclustering
name: Unsupervised Biclustering
def: "A biclustering task focused on methods that simultaneously cluster the rows and columns of an unlabeled input matrix to identify submatrices with coherent patterns." [https://en.wikipedia.org/wiki/Biclustering]
subset: aio:MachineLearningSubset
synonym: "Unsupervised Block Clustering" EXACT []
synonym: "Unsupervised Co-clustering" EXACT []
synonym: "Unsupervised Joint Clustering" EXACT []
synonym: "Unsupervised Two-mode Clustering" EXACT []
synonym: "Unsupervised Two-way Clustering" EXACT []
is_a: aio:Biclustering ! Biclustering

[Term]
id: aio:UnsupervisedClustering
name: Unsupervised Clustering
def: "A clustering focused on methods that group a set of unlabeled objects such that objects in the same group are more similar to each other than to those in other groups." [https://en.wikipedia.org/wiki/Cluster_analysis]
subset: aio:MachineLearningSubset
synonym: "Unsupervised Cluster Analysis" EXACT []
is_a: aio:Clustering ! Clustering

[Term]
id: aio:UnsupervisedLLM
name: Unsupervised LLM
def: "A large language model that is trained solely on unlabeled data using self-supervised objectives like masked language modeling without any supervised fine-tuning." []
subset: aio:ModelSubset
synonym: "self-supervised" RELATED []
synonym: "Unsupervised Large Language Model" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:UnsupervisedLearning
name: Unsupervised Learning
def: "A machine learning task focused on algorithms that learn patterns from unlabeled data." [https://en.wikipedia.org/wiki/Unsupervised_learning]
comment: Algorithms that learn patterns from unlabeled data.
subset: aio:MachineLearningSubset
is_a: aio:MachineLearningTask ! Machine Learning Task

[Term]
id: aio:UnsupervisedPretrainedNetwork
name: Unsupervised Pretrained Network
def: "A network that initializes a discriminative neural net from one trained using an unsupervised criterion." [https://metacademy.org/graphs/concepts/unsupervised_pre_training]
comment: Unsupervised pre-training initializes a discriminative neural net from one trained using an unsupervised criterion, aiding in optimization and overfitting issues.
subset: aio:NetworkSubset
synonym: "UPN" EXACT []
is_a: aio:Network ! Network

[Term]
id: aio:UpSampling1DLayer
name: UpSampling1D Layer
def: "A reshaping layer that upsamples the input by repeating each temporal step size times along the time axis." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/UpSampling1D]
comment: Upsampling layer for 1D inputs. Repeats each temporal step size times along the time axis.
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:UpSampling2DLayer
name: UpSampling2D Layer
def: "A layer that upsamples the input by repeating each row and column size times." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/UpSampling2D]
comment: Upsampling layer for 2D inputs. Repeats the rows and columns of the data by size[0] and size[1] respectively.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:UpSampling3DLayer
name: UpSampling3D Layer
def: "A layer that upsamples the input by repeating each depth" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/UpSampling3D]
comment: Upsampling layer for 3D inputs.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:UseAndInterpretationBias
name: Use And Interpretation Bias
def: "A computational bias characterized by inappropriately analyzing ambiguous stimuli scenarios and events." [https://doi.org/10.6028/NIST.SP.1270]
comment: Bias inappropriately analyzing ambiguous stimuli, scenarios, and events.
subset: aio:BiasSubset
synonym: "Interpretive Bias" EXACT []
is_a: aio:ComputationalBias ! Computational Bias

[Term]
id: aio:UserInteractionBias
name: User Interaction Bias
def: "An individual bias arising when a user imposes their own biases during interaction with data output results etc." [https://doi.org/10.6028/NIST.SP.1270]
comment: Bias arising when a user imposes their own biases during interaction with data, output, results, etc.
subset: aio:BiasSubset
is_a: aio:IndividualBias ! Individual Bias

[Term]
id: aio:VariationalAutoEncoder
name: Variational Auto Encoder
def: "An autoencoder network that imposes a probabilistic structure on the latent space for unsupervised learning." []
comment: Layers: Input, Probabilistic Hidden, Matched Output-Input
subset: aio:NetworkSubset
synonym: "VAE" EXACT []
is_a: aio:AutoEncoderNetwork ! Auto Encoder Network
relationship: BFO:0000051 aio:ProbabilisticHiddenLayer ! has part Probabilistic Hidden Layer

[Term]
id: aio:VectorAutoregression
name: Vector Autoregression
def: "A model that captures the linear interdependencies among multiple time series, where each variable is modeled as a linear function of its own past values and the past values of all other variables in the system." []
subset: aio:ModelSubset
synonym: "VAR" EXACT []
is_a: aio:Model ! Model

[Term]
id: aio:VocabularyReduction
name: Vocabulary Reduction
def: "A data preparation that limits the number of unique tokens in a language model's vocabulary by merging or eliminating less frequent tokens thereby optimizing computational efficiency and resource usage." []
subset: aio:PreprocessingSubset
synonym: "Lexical Simplification" EXACT []
synonym: "Lexicon Pruning" EXACT []
synonym: "Vocabulary Condensation" EXACT []
is_a: aio:DataPreparation ! Data Preparation

[Term]
id: aio:WeightedLayer
name: Weighted Layer
def: "A layer of values to be applied to other cells or neurons in a network." []
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:WrapperLayer
name: Wrapper Layer
def: "A layer that augment the functionality of another layer." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Wrapper]
comment: Abstract wrapper base class. Wrappers take another layer and augment it in various ways. Do not use this class as a layer, it is only an abstract base class. Two usable wrappers are the TimeDistributed and Bidirectional wrappers.
subset: aio:LayerSubset
is_a: aio:Layer ! Layer

[Term]
id: aio:ZeroPadding1DLayer
name: ZeroPadding1D Layer
def: "A reshaping layer that zero-pads the input along the time axis." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding1D]
comment: Zero-padding layer for 1D input (e.g. temporal sequence).
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:ZeroPadding2DLayer
name: ZeroPadding2D Layer
def: "A reshaping layer that zero-pads the input along the height and width dimensions." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding2D]
comment: Zero-padding layer for 2D input (e.g. picture). This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:ZeroPadding3DLayer
name: ZeroPadding3D Layer
def: "A reshaping layer that zero-pads the input along the depth" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding3D]
comment: Zero-padding layer for 3D data (spatial or spatio-temporal).
subset: aio:LayerSubset
is_a: aio:ReshapingLayer ! Reshaping Layer

[Term]
id: aio:ZeroShotLearningLLM
name: Zero-Shot Learning LLM
def: "A large language model which performs tasks or understands concepts it has not explicitly been trained on, demonstrating a high degree of generalization and understanding." []
subset: aio:ModelSubset
synonym: "zero-shot learning" RELATED []
synonym: "Zero-Shot LLM" EXACT []
is_a: aio:LargeLanguageModel ! Large Language Model

[Term]
id: aio:ZeroshotLearning
name: Zero-shot Learning
def: "A deep neural network that predicts classes at test time from classes not observed during training." [https://en.wikipedia.org/wiki/Zero-shot_learning]
subset: aio:NetworkSubset
synonym: "ZSL" EXACT []
is_a: aio:DeepNeuralNetwork ! Deep Neural Network

[Term]
id: aio:node2vec
name: node2vec
def: "A machine learning task designed to learn continuous feature representations for nodes in a graph by optimizing a neighborhood-preserving objective." [https://en.wikipedia.org/wiki/Node2vec]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "N2V" EXACT []
is_a: aio:MachineLearningTask ! Machine Learning Task
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:node2vecCBOW
name: node2vec-CBOW
def: "A node2vec that predicts the current node from a window of surrounding context nodes, with the order of context nodes not influencing prediction." [https://en.wikipedia.org/wiki/Node2vec]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "CBOW" RELATED []
synonym: "N2V-CBOW" EXACT []
is_a: aio:node2vec ! node2vec

[Term]
id: aio:node2vecSkipGram
name: node2vec-SkipGram
def: "A node2vec that uses the current node to predict the surrounding window of context nodes, weighing nearby context nodes more heavily than distant ones." [https://en.wikipedia.org/wiki/Node2vec]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "N2V-SkipGram" EXACT []
synonym: "SkipGram" RELATED []
is_a: aio:node2vec ! node2vec

[Term]
id: aio:tDistributedStochasticNeighborembedding
name: t-Distributed Stochastic Neighbor embedding
def: "A dimensionality reduction for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map." [https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding]
subset: aio:MachineLearningSubset
synonym: "t-SNE" EXACT []
synonym: "tSNE" EXACT []
is_a: aio:DimensionalityReduction ! Dimensionality Reduction

[Term]
id: aio:word2vec
name: word2vec
def: "A machine learning task that generates distributed representations of words by training a shallow neural network model, which aims to predict the context of each word within a corpus. This algorithm captures semantic meanings of words through their contextual usage in the text." [https://en.wikipedia.org/wiki/Word2vec]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "W2V" EXACT []
is_a: aio:MachineLearningTask ! Machine Learning Task
relationship: BFO:0000051 aio:HiddenLayer ! has part Hidden Layer
relationship: BFO:0000051 aio:InputLayer ! has part Input Layer
relationship: BFO:0000051 aio:OutputLayer ! has part Output Layer

[Term]
id: aio:word2vecCBOW
name: word2vec-CBOW
def: "A word2vec that predicts the current word from a window of surrounding context words, ignoring the order of context words." [https://en.wikipedia.org/wiki/Word2vec]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "CBOW" RELATED []
synonym: "W2V-CBOW" EXACT []
is_a: aio:word2vec ! word2vec

[Term]
id: aio:word2vecSkipGram
name: word2vec-SkipGram
def: "A word2vec that predicts surrounding context words from the current word, giving more weight to nearby context words than distant ones." [https://en.wikipedia.org/wiki/Word2vec]
comment: Layers: Input, Hidden, Output
subset: aio:NetworkSubset
synonym: "SkipGram" RELATED []
synonym: "W2V-SkipGram" EXACT []
is_a: aio:word2vec ! word2vec

[Typedef]
id: BFO:0000050
name: part of
def: "A core relation that holds between a part and its whole" []

[Typedef]
id: BFO:0000051
name: has part
def: "A core relation that holds between a whole and its part" []