The HSIC Bottleneck: Deep Learning without Back-Propagation
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Updated
Dec 13, 2020 - Python
The HSIC Bottleneck: Deep Learning without Back-Propagation
Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics
A lightweight didactic library of kernel methods using the back-end JAX.
Codes used for the results in the paper: Sensitivity Analysis for a long-time clogging simulation code.
Derivatives of Kernel methods: Theory and toy examples with Earth System Data
Feature extraction with Hilbert-Schmidt Independence Criterion (HSIC)
In this repo, I will be looking at how to choose the best parameters for HSIC alignment.
Sensitivity Analysis in space engineering using modern machine learning tools.
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