From fda8b036231d789eb3893cc38014ada8b0f14e0a Mon Sep 17 00:00:00 2001 From: AndreaCossu Date: Mon, 11 Jul 2022 10:17:33 +0000 Subject: [PATCH] Updated papers files --- README.md | 24 +- embedding-plot.html | 2 +- embedding.json | 2 +- papers.md | 2054 ++++++++++++++++++++++++------------------- 4 files changed, 1183 insertions(+), 899 deletions(-) diff --git a/README.md b/README.md index 1c70a11..9f8187e 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ ContinualAI logo

-Continual Learning papers list, curated by ContinualAI. **Search among 332 papers!** +Continual Learning papers list, curated by ContinualAI. **Search among 338 papers!** You can browse the list in this file or interactively on the [ContinualAI website](https://www.continualai.org/papers/). @@ -123,14 +123,16 @@ In this section we maintain a list of all applicative papers produced on continu ### Architectural Methods -**33 papers** +**35 papers** In this section we collect all the papers introducing a continual learning strategy employing some architectural methods. - [Provable and Efficient Continual Representation Learning](http://arxiv.org/abs/2203.02026) by Yingcong Li, Mingchen Li, M. Salman Asif and Samet Oymak. *arXiv*, 2022. - [Architecture Matters in Continual Learning](http://arxiv.org/abs/2202.00275) by Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Timothy Nguyen, Razvan Pascanu, Dilan Gorur and Mehrdad Farajtabar. *arXiv*, 2022. +- [Continual Learning with Node-Importance Based Adaptive Group Sparse Regularization](http://arxiv.org/abs/2003.13726) by Sangwon Jung, Hongjoon Ahn, Sungmin Cha and Taesup Moon. , 2021. - [Structured Ensembles: An Approach to Reduce the Memory Footprint of Ensemble Methods](https://linkinghub.elsevier.com/retrieve/pii/S0893608021003579) by Jary Pomponi, Simone Scardapane and Aurelio Uncini. *Neural Networks*, 407--418, 2021. - [Continual Learning via Bit-Level Information Preserving](https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Continual_Learning_via_Bit-Level_Information_Preserving_CVPR_2021_paper.html) by Yujun Shi, Li Yuan, Yunpeng Chen and Jiashi Feng. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, 16674--16683, 2021. +- [Modular Dynamic Neural Network: A Continual Learning Architecture](https://www.mdpi.com/2076-3417/11/24/12078) by Daniel Turner, Pedro J. S. Cardoso and João M. F. Rodrigues. *Applied Sciences*, 12078, 2021. - [Continual Learning with Adaptive Weights (CLAW)](https://openreview.net/forum?id=Hklso24Kwr) by Tameem Adel, Han Zhao and Richard E Turner. *International Conference on Learning Representations*, 2020. [cifar] [mnist] [omniglot] - [Continual Learning with Gated Incremental Memories for Sequential Data Processing](http://arxiv.org/abs/2004.04077) by Andrea Cossu, Antonio Carta and Davide Bacciu. *Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN 2020)*, 2020. [mnist] [rnn] - [Continual Learning in Recurrent Neural Networks](https://openreview.net/forum?id=8xeBUgD8u9) by Benjamin Ehret, Christian Henning, Maria Cervera, Alexander Meulemans, Johannes Von Oswald and Benjamin F. Grewe. *International Conference on Learning Representations*, 2020. [audio] [rnn] @@ -188,8 +190,8 @@ In this section we list all the papers related to bioinspired continual learning - [A Biologically Plausible Audio-Visual Integration Model for Continual Learning](http://arxiv.org/abs/2007.08855) by Wenjie Chen, Fengtong Du, Ye Wang and Lihong Cao. *IJCNN*, 2021. - [Synaptic Metaplasticity in Binarized Neural Networks](https://www.nature.com/articles/s41467-021-22768-y) by Axel Laborieux, Maxence Ernoult, Tifenn Hirtzlin and Damien Querlioz. *Nature Communications*, 2549, 2021. - [Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks](https://www.frontiersin.org/article/10.3389/fnins.2020.00007/full) by Jason M. Allred and Kaushik Roy. *Frontiers in Neuroscience*, 7, 2020. [spiking] -- [Cognitively-Inspired Model for Incremental Learning Using a Few Examples](https://openaccess.thecvf.com/content_CVPRW_2020/html/w15/Ayub_Cognitively-Inspired_Model_for_Incremental_Learning_Using_a_Few_Examples_CVPRW_2020_paper.html) by A. Ayub and A. R. Wagner. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops*, 2020. [cifar] [cubs] [dual] - [Storing Encoded Episodes as Concepts for Continual Learning](https://arxiv.org/abs/2007.06637 http://arxiv.org/abs/2007.06637) by Ali Ayub and Alan R. Wagner. *arXiv*, 2020. [generative] [imagenet] [mnist] +- [Cognitively-Inspired Model for Incremental Learning Using a Few Examples](https://openaccess.thecvf.com/content_CVPRW_2020/html/w15/Ayub_Cognitively-Inspired_Model_for_Incremental_Learning_Using_a_Few_Examples_CVPRW_2020_paper.html) by A. Ayub and A. R. Wagner. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops*, 2020. [cifar] [cubs] [dual] - [Spiking Neural Predictive Coding for Continual Learning from Data Streams](http://arxiv.org/abs/1908.08655) by and Alexander Ororbia. *arXiv*, 2020. [spiking] - [Brain-like Replay for Continual Learning with Artificial Neural Networks](https://baicsworkshop.github.io/pdf/BAICS_8.pdf) by Gido M. van de Ven, Hava T. Siegelmann and Andreas S. Tolias. *International Conference on Learning Representations (Workshop on Bridging AI and Cognitive Science)*, 2020. [cifar] - [Selfless Sequential Learning](https://openreview.net/forum?id=Bkxbrn0cYX) by Rahaf Aljundi, Marcus Rohrbach and Tinne Tuytelaars. *ICLR*, 2019. [cifar] [mnist] [sparsity] @@ -218,8 +220,8 @@ In this section we list all the major contributions trying to understand catastr - [Architecture Matters in Continual Learning](http://arxiv.org/abs/2202.00275) by Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Timothy Nguyen, Razvan Pascanu, Dilan Gorur and Mehrdad Farajtabar. *arXiv*, 2022. - [Continual Learning in the Teacher-Student Setup: Impact of Task Similarity](http://proceedings.mlr.press/v139/lee21e.html) by Sebastian Lee, Sebastian Goldt and Andrew Saxe. *International Conference on Machine Learning*, 6109--6119, 2021. -- [Understanding Continual Learning Settings with Data Distribution Drift Analysis](http://arxiv.org/abs/2104.01678) by Timothée Lesort, Massimo Caccia and Irina Rish. *arXiv*, 2021. - [Continual Learning in Deep Networks: An Analysis of the Last Layer](http://arxiv.org/abs/2106.01834) by Timothée Lesort, Thomas George and Irina Rish. *arXiv*, 2021. +- [Understanding Continual Learning Settings with Data Distribution Drift Analysis](http://arxiv.org/abs/2104.01678) by Timothée Lesort, Massimo Caccia and Irina Rish. *arXiv*, 2021. - [Wide Neural Networks Forget Less Catastrophically](http://arxiv.org/abs/2110.11526) by Seyed Iman Mirzadeh, Arslan Chaudhry, Huiyi Hu, Razvan Pascanu, Dilan Gorur and Mehrdad Farajtabar. *arXiv*, 2021. - [Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics](https://openreview.net/forum?id=LhY8QdUGSuw) by Vinay Venkatesh Ramasesh, Ethan Dyer and Maithra Raghu. *International Conference on Learning Representations*, 2021. - [Does Continual Learning = Catastrophic Forgetting?](http://arxiv.org/abs/2101.07295) by Anh Thai, Stefan Stojanov, Isaac Rehg and James M. Rehg. *arXiv*, 2021. @@ -445,12 +447,12 @@ In this section we list all the other papers not appearing in at least one of th - [Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning](https://openreview.net/forum?id=LXMSvPmsm0g) by Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang and Zhangyang Wang. *International Conference on Learning Representations*, 2020. - [Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis](http://arxiv.org/abs/1909.01520) by Tyler L Hayes and Christopher Kanan. *CLVision Workshop at CVPR 2020*, 1--15, 2020. [core50] [imagenet] - [Continual Learning with Bayesian Neural Networks for Non-Stationary Data](https://iclr.cc/virtual_2020/poster_SJlsFpVtDB.html) by Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt and Stephan Günnemann. *Eighth International Conference on Learning Representations*, 2020. [bayes] -- [Continual Learning Using Task Conditional Neural Networks](http://arxiv.org/abs/2005.05080) by Honglin Li, Payam Barnaghi, Shirin Enshaeifar and Frieder Ganz. *arXiv*, 2020. [cifar] [mnist] - [Energy-Based Models for Continual Learning](http://arxiv.org/abs/2011.12216) by Shuang Li, Yilun Du, Gido M. van de Ven, Antonio Torralba and Igor Mordatch. *arXiv*, 2020. [cifar] [experimental] [mnist] -- [Continual Universal Object Detection](http://arxiv.org/abs/2002.05347) by Xialei Liu, Hao Yang, Avinash Ravichandran, Rahul Bhotika and Stefano Soatto. *arXiv*, 2020. +- [Continual Learning Using Task Conditional Neural Networks](http://arxiv.org/abs/2005.05080) by Honglin Li, Payam Barnaghi, Shirin Enshaeifar and Frieder Ganz. *arXiv*, 2020. [cifar] [mnist] - [Mnemonics Training: Multi-Class Incremental Learning without Forgetting](http://arxiv.org/abs/2002.10211) by Yaoyao Liu, An-An Liu, Yuting Su, Bernt Schiele and Qianru Sun. *arXiv*, 2020. [cifar] [imagenet] -- [Structured Compression and Sharing of Representational Space for Continual Learning](http://arxiv.org/abs/2001.08650) by Gobinda Saha, Isha Garg, Aayush Ankit and Kaushik Roy. *arXiv*, 2020. [cifar] [mnist] +- [Continual Universal Object Detection](http://arxiv.org/abs/2002.05347) by Xialei Liu, Hao Yang, Avinash Ravichandran, Rahul Bhotika and Stefano Soatto. *arXiv*, 2020. - [Gradient Projection Memory for Continual Learning](https://openreview.net/forum?id=3AOj0RCNC2) by Gobinda Saha and Kaushik Roy. *International Conference on Learning Representations*, 2020. +- [Structured Compression and Sharing of Representational Space for Continual Learning](http://arxiv.org/abs/2001.08650) by Gobinda Saha, Isha Garg, Aayush Ankit and Kaushik Roy. *arXiv*, 2020. [cifar] [mnist] - [Gated Linear Networks](http://arxiv.org/abs/1910.01526) by Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt and Marcus Hutter. *arXiv*, 2020. - [Lifelong Graph Learning](http://arxiv.org/abs/2009.00647) by Chen Wang, Yuheng Qiu and Sebastian Scherer. *arXiv*, 2020. [graph] - [Superposition of Many Models into One](http://arxiv.org/abs/1902.05522) by Brian Cheung, Alex Terekhov, Yubei Chen, Pulkit Agrawal and Bruno Olshausen. *arXiv*, 2019. [cifar] [mnist] @@ -480,13 +482,14 @@ In this section we list all the other papers not appearing in at least one of th ### Regularization Methods -**28 papers** +**29 papers** In this section we collect all the papers introducing a continual learning strategy employing some regularization methods. - [Using Hindsight to Anchor Past Knowledge in Continual Learning](http://arxiv.org/abs/2002.08165) by Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip Torr and David Lopez-Paz. *arXiv*, 2021. - [Contrastive Continual Learning with Feature Propagation](http://arxiv.org/abs/2112.01713) by Xuejun Han and Yuhong Guo. *arXiv:2112.01713 [cs]*, 2021. - [Gradient Projection Memory for Continual Learning](http://arxiv.org/abs/2103.09762) by Gobinda Saha, Isha Garg and Kaushik Roy. *arXiv:2103.09762 [cs]*, 2021. +- [Gradient Projection Memory for Continual Learning](http://arxiv.org/abs/2103.09762) by Gobinda Saha, Isha Garg and Kaushik Roy. , 2021. - [Modeling the Background for Incremental Learning in Semantic Segmentation](http://arxiv.org/abs/2002.00718) by Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci and Barbara Caputo. *CVPR*, 9233--9242, 2020. - [PLOP: Learning without Forgetting for Continual Semantic Segmentation](https://arxiv.org/abs/2011.11390) by Arthur Douillard, Yifu Chen, Arnaud Dapogny and Matthieu Cord. *arXiv*, 2020. - [Insights from the Future for Continual Learning](https://arxiv.org/abs/2006.13748) by Arthur Douillard, Eduardo Valle, Charles Ollion, Thomas Robert and Matthieu Cord. *arXiv*, 2020. @@ -515,10 +518,11 @@ In this section we collect all the papers introducing a continual learning strat ### Rehearsal Methods -**28 papers** +**29 papers** In this section we collect all the papers introducing a continual learning strategy employing some rehearsal methods. +- [It's All About Consistency: A Study on Memory Composition for Replay-Based Methods in Continual Learning](http://arxiv.org/abs/2207.01145) by Julio Hurtado, Alain Raymond-Saez, Vladimir Araujo, Vincenzo Lomonaco and Davide Bacciu. , 2022. - [Foundational Models for Continual Learning: An Empirical Study of Latent Replay](http://arxiv.org/abs/2205.00329) by Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish and Laurent Charlin. *arXiv*, 2022. - [Using Hindsight to Anchor Past Knowledge in Continual Learning](http://arxiv.org/abs/2002.08165) by Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip Torr and David Lopez-Paz. *arXiv*, 2021. - [Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams](https://openaccess.thecvf.com/content/ICCV2021/html/De_Lange_Continual_Prototype_Evolution_Learning_Online_From_Non-Stationary_Data_Streams_ICCV_2021_paper.html) by Matthias De Lange and Tinne Tuytelaars. *Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)*, 8250--8259, 2021. [cifar] [framework] [mnist] [vision] @@ -536,8 +540,8 @@ In this section we collect all the papers introducing a continual learning strat - [Graph-Based Continual Learning](https://openreview.net/forum?id=HHSEKOnPvaO) by Binh Tang and David S. Matteson. *International Conference on Learning Representations*, 2020. - [Brain-Inspired Replay for Continual Learning with Artificial Neural Networks](https://www.nature.com/articles/s41467-020-17866-2) by Gido M. van de Ven, Hava T. Siegelmann and Andreas S. Tolias. *Nature Communications*, 2020. [cifar] [framework] [generative] [mnist] - [Continual Learning with Hypernetworks](https://openreview.net/forum?id=SJgwNerKvB) by Johannes von Oswald, Christian Henning, João Sacramento and Benjamin F Grewe. *International Conference on Learning Representations*, 2020. [cifar] [mnist] -- [Gradient Based Sample Selection for Online Continual Learning](http://papers.nips.cc/paper/9354-gradient-based-sample-selection-for-online-continual-learning.pdf) by Rahaf Aljundi, Min Lin, Baptiste Goujaud and Yoshua Bengio. *Advances in Neural Information Processing Systems 32*, 11816--11825, 2019. [cifar] [mnist] - [Online Continual Learning with Maximal Interfered Retrieval](http://papers.nips.cc/paper/9357-online-continual-learning-with-maximal-interfered-retrieval.pdf) by Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin and Lucas Page-Caccia. *Advances in Neural Information Processing Systems 32*, 11849--11860, 2019. [cifar] [mnist] +- [Gradient Based Sample Selection for Online Continual Learning](http://papers.nips.cc/paper/9354-gradient-based-sample-selection-for-online-continual-learning.pdf) by Rahaf Aljundi, Min Lin, Baptiste Goujaud and Yoshua Bengio. *Advances in Neural Information Processing Systems 32*, 11816--11825, 2019. [cifar] [mnist] - [IL2M: Class Incremental Learning With Dual Memory](https://doi.org/10.1109/ICCV.2019.00067) by Eden Belouadah and Adrian Popescu. *2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019*, 583--592, 2019. - [On Tiny Episodic Memories in Continual Learning](https://github.com/facebookresearch/agem http://arxiv.org/abs/1902.10486) by Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip H S Torr and Marc'Aurelio Ranzato. *arXiv*, 2019. [cifar] [imagenet] [mnist] - [Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients](http://arxiv.org/abs/1904.10644) by Yu Chen, Tom Diethe and Neil Lawrence. *arXiv*, 2019. [bayes] diff --git a/embedding-plot.html b/embedding-plot.html index 75f614f..11929c5 100644 --- a/embedding-plot.html +++ b/embedding-plot.html @@ -66,6 +66,6 @@ * Copyright (c) 2014-2015, Jon Schlinkert. * Licensed under the MIT License. */ -"use strict";var n,i="";e.exports=function(t,e){if("string"!=typeof t)throw new TypeError("expected a string");if(1===e)return t;if(2===e)return t+t;var r=t.length*e;if(n!==t||void 0===n)n=t,i="";else if(i.length>=r)return i.substr(0,r);for(;r>i.length&&e>1;)1&e&&(i+=t),e>>=1,t+=t;return i=(i+=t).substr(0,r)}},{}],278:[function(t,e,r){(function(t){(function(){e.exports=t.performance&&t.performance.now?function(){return performance.now()}:Date.now||function(){return+new Date}}).call(this)}).call(this,void 0!==n?n:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{}],279:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.length,r=t[t.length-1],n=e,i=e-2;i>=0;--i){var a=r,o=t[i];(l=o-((r=a+o)-a))&&(t[--n]=r,r=l)}var 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t={not_string:/[^s]/,not_bool:/[^t]/,not_type:/[^T]/,not_primitive:/[^v]/,number:/[diefg]/,numeric_arg:/[bcdiefguxX]/,json:/[j]/,not_json:/[^j]/,text:/^[^\x25]+/,modulo:/^\x25{2}/,placeholder:/^\x25(?:([1-9]\d*)\$|\(([^)]+)\))?(\+)?(0|'[^$])?(-)?(\d+)?(?:\.(\d+))?([b-gijostTuvxX])/,key:/^([a-z_][a-z_\d]*)/i,key_access:/^\.([a-z_][a-z_\d]*)/i,index_access:/^\[(\d+)\]/,sign:/^[+-]/};function e(t){return i(o(t),arguments)}function n(t,r){return e.apply(null,[t].concat(r||[]))}function i(r,n){var 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6:u.push([t-.5-.25*(-n-r+a+i)/(n-r+i-a),e-.5-.25*(-i-r+a+n)/(i-r+n-a)]);break;case 7:u.push([t-.75-.25*(a+i-2*h)/(i-a),e-.75-.25*(a+n-2*h)/(n-a)]);break;case 8:u.push([t-.75-.25*(-a-i+2*h)/(a-i),e-.75-.25*(-a-n+2*h)/(a-n)]);break;case 9:u.push([t-.5-.25*(n+r+-a-i)/(r-n+a-i),e-.5-.25*(i+r+-a-n)/(r-i+a-n)]);break;case 10:u.push([t-.5-.5*(-n-r-a-i+4*h)/(n-r+a-i),e-.5]);break;case 11:u.push([t-.25-.25*(-a-i+2*h)/(a-i),e-.75-.25*(i+r-2*h)/(r-i)]);break;case 12:u.push([t-.5,e-.5-.5*(-i-r-a-n+4*h)/(i-r+a-n)]);break;case 13:u.push([t-.75-.25*(n+r-2*h)/(r-n),e-.25-.25*(-a-n+2*h)/(a-n)]);break;case 14:u.push([t-.25-.25*(-n-r+2*h)/(n-r),e-.25-.25*(-i-r+2*h)/(i-r)]);break;case 15:u.push([t-.5,e-.5])}},cell:function(t,e,r,n,i,a,o,s,l){i?s.push([t,e]):s.push([e,t])}});return function(t,e){var r=[],i=[];return n(t,r,i,e),{positions:r,cells:i}}}};var o={}},{"ndarray-extract-contour":251,"zero-crossings":318}],303:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function 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p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;"number"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I0?r.pop():new ArrayBuffer(t)}function d(t){return new Uint8Array(p(t),0,t)}function m(t){return new Uint16Array(p(2*t),0,t)}function g(t){return new Uint32Array(p(4*t),0,t)}function v(t){return new Int8Array(p(t),0,t)}function y(t){return new Int16Array(p(2*t),0,t)}function x(t){return new 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p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var 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Salman and Oymak, Samet)", "Architecture Matters in Continual Learning (Mirzadeh, Seyed Iman and Chaudhry, Arslan and Yin, Dong and Nguyen, Timothy and Pascanu, Razvan and Gorur, Dilan and Farajtabar, Mehrdad)", "Structured Ensembles: An Approach to Reduce the Memory Footprint of Ensemble Methods (Pomponi, Jary and Scardapane, Simone and Uncini, Aurelio)", "Continual Learning with Adaptive Weights (CLAW) (Adel, Tameem and Zhao, Han and Turner, Richard E)", "Continual Learning with Gated Incremental Memories for Sequential Data Processing (Cossu, Andrea and Carta, Antonio and Bacciu, Davide)", "Continual Learning in Recurrent Neural Networks (Ehret, Benjamin and Henning, Christian and Cervera, Maria and Meulemans, Alexander and Oswald, Johannes Von and Grewe, Benjamin F.)", "Explainability in Deep Reinforcement Learning (Heuillet, Alexandre and Couthouis, Fabien and D\u00edaz-Rodr\\\u01f5uez, Natalia)", "Bayesian Nonparametric Weight Factorization for Continual Learning (Mehta, Nikhil and Liang, Kevin J and Carin, Lawrence)", "SpaceNet: Make Free Space For Continual Learning (Sokar, Ghada and Mocanu, Decebal Constantin and Pechenizkiy, Mykola)", "Efficient Continual Learning with Modular Networks and Task-Driven Priors (Veniat, Tom and Denoyer, Ludovic and Ranzato, Marc'Aurelio)", "Progressive Memory Banks for Incremental Domain Adaptation (Asghar, Nabiha and Mou, Lili and Selby, Kira A and Pantasdo, Kevin D and Poupart, Pascal and Jiang, Xin)", "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments (Ashfahani, Andri and Pratama, Mahardhika)", "Compacting, Picking and Growing for Unforgetting Continual Learning (Hung, Steven C Y and Tu, Cheng-Hao and Wu, Cheng-En and Chen, Chien-Hung and Chan, Yi-Ming and Chen, Chu-Song)", "Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (Li, Xilai and Zhou, Yingbo and Wu, Tianfu and Socher, Richard and Xiong, Caiming)", "Towards AutoML in the Presence of Drift: First Results (Madrid, Jorge G. and Escalante, Hugo Jair and Morales, Eduardo F. and Tu, Wei-Wei and Yu, Yang and Sun-Hosoya, Lisheng and Guyon, Isabelle and Sebag, Michele)", "Continual Unsupervised Representation Learning (Rao, Dushyant and Visin, Francesco and Rusu, Andrei A and Teh, Yee Whye and Pascanu, Razvan and Hadsell, Raia)", "A Progressive Model to Enable Continual Learning for Semantic Slot Filling (Shen, Yilin and Zeng, Xiangyu and Jin, Hongxia)", "Adaptive Compression-based Lifelong Learning (Srivastava, Shivangi and Berman, Maxim and Blaschko, Matthew B and Tuia, Devis)", "Frosting Weights for Better Continual Training (Zhu, Xiaofeng and Liu, Feng and Trajcevski, Goce and Wang, Dingding)", "Dynamic Few-Shot Visual Learning Without Forgetting (Gidaris, Spyros and Komodakis, Nikos)", "HOUDINI: Lifelong Learning as Program Synthesis (Valkov, Lazar and Chaudhari, Dipak and Srivastava, Akash and Sutton, Charles and Chaudhuri, Swarat)", "Reinforced Continual Learning (Xu, Ju and Zhu, Zhanxing)", "Lifelong Learning With Dynamically Expandable Networks (Yoon, Jaehong and Yang, Eunho and Lee, Jeongtae and Hwang, Sung Ju)", "Expert Gate: Lifelong Learning with a Network of Experts (Aljundi, Rahaf and Chakravarty, Punarjay and Tuytelaars, Tinne)", "Neurogenesis Deep Learning (Draelos, Timothy John and Miner, Nadine E and Lamb, Christopher and Cox, Jonathan A and Vineyard, Craig Michael and Carlson, Kristofor David and Severa, William Mark and James, Conrad D and Aimone, James Bradley)", "Net2Net: Accelerating Learning via Knowledge Transfer (Chen, Tianqi and Goodfellow, Ian and Shlens, Jonathon)", "Continual Learning through Evolvable Neural Turing Machines (Luders, Benno and Schlager, Mikkel and Risi, Sebastian)", "Progressive Neural Networks (Rusu, Andrei A and Rabinowitz, Neil C and Desjardins, Guillaume and Soyer, Hubert and Kirkpatrick, James and Kavukcuoglu, Koray and Pascanu, Razvan and Hadsell, Raia)", "Knowledge Transfer in Deep Block-Modular Neural Networks (Terekhov, Alexander V. and Montone, Guglielmo and O'Regan, J. Kevin)", "ELLA: An Efficient Lifelong Learning Algorithm (Ruvolo, Paul and Eaton, Eric)", "A Self-Organising Network That Grows When Required (Marsland, Stephen and Shapiro, Jonathan and Nehmzow, Ulrich)", "vCLIMB: A Novel Video Class Incremental Learning Benchmark (Villa, Andr\u00e9s and Alhamoud, Kumail and Alc\u00e1zar, Juan Le\u00f3n and Heilbron, Fabian Caba and Escorcia, Victor and Ghanem, Bernard)", "Is Class-Incremental Enough for Continual Learning? (Cossu, Andrea and Graffieti, Gabriele and Pellegrini, Lorenzo and Maltoni, Davide and Bacciu, Davide and Carta, Antonio and Lomonaco, Vincenzo)", "Efficient Continual Learning with Modular Networks and Task-Driven Priors (Veniat, Tom and Denoyer, Ludovic and Ranzato, Marc'Aurelio)", "Defining Benchmarks for Continual Few-Shot Learning (Antoniou, Antreas and Patacchiola, Massimiliano and Ochal, Mateusz and Storkey, Amos)", "Evaluating Online Continual Learning with CALM (Kruszewski, Germ\u00e1n and Sorodoc, Ionut-Teodor and Mikolov, Tomas)", "Continual Reinforcement Learning in 3D Non-Stationary Environments (Lomonaco, Vincenzo and Desai, Karan and Culurciello, Eugenio and Maltoni, Davide)", "OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning (She, Qi and Feng, Fan and Hao, Xinyue and Yang, Qihan and Lan, Chuanlin and Lomonaco, Vincenzo and Shi, Xuesong and Wang, Zhengwei and Guo, Yao and Zhang, Yimin and Qiao, Fei and Chan, Rosa H M)", "New Metrics and Experimental Paradigms for Continual Learning (Hayes, Tyler L. and Kemker, Ronald and Cahill, Nathan D. and Kanan, Christopher)", "CORe50: A New Dataset and Benchmark for Continuous Object Recognition (Lomonaco, Vincenzo and Maltoni, Davide)", "A Biologically Plausible Audio-Visual Integration Model for Continual Learning (Chen, Wenjie and Du, Fengtong and Wang, Ye and Cao, Lihong)", "Synaptic Metaplasticity in Binarized Neural Networks (Laborieux, Axel and Ernoult, Maxence and Hirtzlin, Tifenn and Querlioz, Damien)", "Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks (Allred, Jason M. and Roy, Kaushik)", "Cognitively-Inspired Model for Incremental Learning Using a Few Examples (Ayub, A. and Wagner, A. R.)", "Storing Encoded Episodes as Concepts for Continual Learning (Ayub, Ali and Wagner, Alan R.)", "Spiking Neural Predictive Coding for Continual Learning from Data Streams (Ororbia, Alexander)", "Brain-like Replay for Continual Learning with Artificial Neural Networks (van de Ven, Gido M. and Siegelmann, Hava T. and Tolias, Andreas S.)", "Selfless Sequential Learning (Aljundi, Rahaf and Rohrbach, Marcus and Tuytelaars, Tinne)", "Backpropamine: Training Self-Modifying Neural Networks with Differentiable Neuromodulated Plasticity (Miconi, Thomas and Rawal, Aditya and Clune, Jeff and Stanley, Kenneth O)", "Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations (Ororbia, Alexander and Mali, Ankur and Giles, C Lee and Kifer, Daniel)", "Lifelong Neural Predictive Coding: Sparsity Yields Less Forgetting When Learning Cumulatively (Ororbia, Alexander and Mali, Ankur and Kifer, Daniel and Giles, C Lee)", "FearNet: Brain-Inspired Model for Incremental Learning (Kemker, Ronald and Kanan, Christopher)", "Differentiable Plasticity: Training Plastic Neural Networks with Backpropagation (Miconi, Thomas and Stanley, Kenneth and Clune, Jeff)", "Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (Parisi, German I and Tani, Jun and Weber, Cornelius and Wermter, Stefan)", "Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World (Garg, Sahil and Rish, Irina and Cecchi, Guillermo and Lozano, Aurelie)", "Diffusion-Based Neuromodulation Can Eliminate Catastrophic Forgetting in Simple Neural Networks (Velez, Roby and Clune, Jeff)", "How Do Neurons Operate on Sparse Distributed Representations? A Mathematical Theory of Sparsity, Neurons and Active Dendrites (Ahmad, Subutai and Hawkins, Jeff)", "Continuous Online Sequence Learning with an Unsupervised Neural Network Model (Cui, Yuwei and Ahmad, Subutai and Hawkins, Jeff)", "Mitigation of Catastrophic Forgetting in Recurrent Neural Networks Using a Fixed Expansion Layer (Coop, Robert and Arel, Itamar)", "Compete to Compute (Srivastava, Rupesh Kumar and Masci, Jonathan and Kazerounian, Sohrob and Gomez, Faustino and Schmidhuber, J\u00fcrgen)", "Mitigation of Catastrophic Interference in Neural Networks Using a Fixed Expansion Layer (Coop, Robert and Arel, Itamar)", "Synaptic Plasticity: Taming the Beast (Abbott, L F and Nelson, Sacha B)", "Architecture Matters in Continual Learning (Mirzadeh, Seyed Iman and Chaudhry, Arslan and Yin, Dong and Nguyen, Timothy and Pascanu, Razvan and Gorur, Dilan and Farajtabar, Mehrdad)", "Continual Learning in the Teacher-Student Setup: Impact of Task Similarity (Lee, Sebastian and Goldt, Sebastian and Saxe, Andrew)", "Understanding Continual Learning Settings with Data Distribution Drift Analysis (Lesort, Timoth\u00e9e and Caccia, Massimo and Rish, Irina)", "Continual Learning in Deep Networks: An Analysis of the Last Layer (Lesort, Timoth\u00e9e and George, Thomas and Rish, Irina)", "Wide Neural Networks Forget Less Catastrophically (Mirzadeh, Seyed Iman and Chaudhry, Arslan and Hu, Huiyi and Pascanu, Razvan and Gorur, Dilan and Farajtabar, Mehrdad)", "Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics (Ramasesh, Vinay Venkatesh and Dyer, Ethan and Raghu, Maithra)", "Does Continual Learning = Catastrophic Forgetting? (Thai, Anh and Stojanov, Stefan and Rehg, Isaac and Rehg, James M.)", "Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget (Davidson, Guy and Mozer, Michael C)", "Understanding the Role of Training Regimes in Continual Learning (Mirzadeh, Seyed Iman and Farajtabar, Mehrdad and Pascanu, Razvan and Ghasemzadeh, Hassan)", "Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization (Nguyen, Giang and Chen, Shuan and Do, Thao and Jun, Tae Joon and Choi, Ho-Jin and Kim, Daeyoung)", "Toward Understanding Catastrophic Forgetting in Continual Learning (Nguyen, Cuong V and Achille, Alessandro and Lam, Michael and Hassner, Tal and Mahadevan, Vijay and Soatto, Stefano)", "A Study on Catastrophic Forgetting in Deep LSTM Networks (Schak, Monika and Gepperth, Alexander)", "An Empirical Study of Example Forgetting during Deep Neural Network Learning (Toneva, Mariya and Sordoni, Alessandro and des Combes, Remi Tachet and Trischler, Adam and Bengio, Yoshua and Gordon, Geoffrey J)", "Localizing Catastrophic Forgetting in Neural Networks (Wiewel, Felix and Yang, Bin)", "An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks (Goodfellow, Ian J. and Mirza, Mehdi and Xiao, Da and Courville, Aaron and Bengio, Yoshua)", "How Does a Brain Build a Cognitive Code? (Grossberg, Stephen)", "The Organization of Behavior: A Neuropsychological Theory (Hebb, D O)", "Pseudo-Recurrent Connectionist Networks: An Approach to the 'Sensitivity-Stability' Dilemma (French, Robert)", "CHILD: A First Step Towards Continual Learning (Ring, Mark B)", "Learning in the Presence of Concept Drift and Hidden Contexts (Widmer, Gerhard and Kubat, Miroslav)", "Using Semi-Distributed Representations to Overcome Catastrophic Forgetting in Connectionist Networks (French, Robert)", "Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions (Ratcliff, R.)", "The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network (Carpenter, Gail A. and Grossberg, Stephen)", "How Does a Brain Build a Cognitive Code? (Grossberg, Stephen)", "Few-Shot Continual Learning: A Brain-Inspired Approach (Wang, Liyuan and Li, Qian and Zhong, Yi and Zhu, Jun)", "Defining Benchmarks for Continual Few-Shot Learning (Antoniou, Antreas and Patacchiola, Massimiliano and Ochal, Mateusz and Storkey, Amos)", "Tell Me What This Is: Few-Shot Incremental Object Learning by a Robot (Ayub, Ali and Wagner, Alan R.)", "La-MAML: Look-ahead Meta Learning for Continual Learning (Gupta, Gunshi and Yadav, Karmesh and Paull, Liam)", "iTAML: An Incremental Task-Agnostic Meta-learning Approach (Rajasegaran, Jathushan and Khan, Salman and Hayat, Munawar and Khan, Fahad Shahbaz and Shah, Mubarak)", "Wandering within a World: Online Contextualized Few-Shot Learning (Ren, Mengye and Iuzzolino, Michael L and Mozer, Michael C and Zemel, Richard S)", "Few-Shot Class-Incremental Learning (Tao, X. and X., Hong and Chang, X. and Dong, S. and Wei, X. and Gong, Y.)", "Few-Shot Class-Incremental Learning via Feature Space Composition (Zhao, H. and Fu, Y. and Li, X. and Li, S. and Omar, B. and Li, X.)", "Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning (Caccia, Massimo and Rodriguez, Pau and Ostapenko, Oleksiy and Normandin, Fabrice and Lin, Min and Caccia, Lucas and Laradji, Issam and Rish, Irina and Lacoste, Alexande and Vazquez, David and Charlin, Laurent)", "Continuous Meta-Learning without Tasks (Harrison, James and Sharma, Apoorva and Finn, Chelsea and Pavone, Marco)", "Task Agnostic Continual Learning via Meta Learning (He, Xu and Sygnowski, Jakub and Galashov, Alexandre and Rusu, Andrei A and Teh, Yee Whye and Pascanu, Razvan)", "Reconciling Meta-Learning and Continual Learning with Online Mixtures of Tasks (Jerfel, Ghassen and Grant, Erin and Griffiths, Tom and Heller, Katherine A)", "Lifetime Policy Reuse and the Importance of Task Capacity (Bossens, David M. and Sobey, Adam J.)", "Unsupervised Lifelong Learning with Curricula (He, Yi and Chen, Sheng and Wu, Baijun and Yuan, Xu and Wu, Xindong)", "Continuous Coordination As a Realistic Scenario for Lifelong Learning (Nekoei, Hadi and Badrinaaraayanan, Akilesh and Courville, Aaron and Chandar, Sarath)", "Reducing Catastrophic Forgetting When Evolving Neural Networks (Early, Joseph)", "A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning (Garcia, Francisco M and Thomas, Philip S)", "Policy Consolidation for Continual Reinforcement Learning (Kaplanis, Christos and Shanahan, Murray and Clopath, Claudia)", "Continual Learning Exploiting Structure of Fractal Reservoir Computing (Kobayashi, Taisuke and Sugino, Toshiki)", "Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL (Nagabandi, Anusha and Finn, Chelsea and Levine, Sergey)", "Leaky Tiling Activations: A Simple Approach to Learning Sparse Representations Online (Pan, Yangchen and Banman, Kirby and White, Martha)", "Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference (Riemer, Matthew and Cases, Ignacio and Ajemian, Robert and Liu, Miao and Rish, Irina and Tu, Yuhai and Tesauro, Gerald)", "Experience Replay for Continual Learning (Rolnick, David and Ahuja, Arun and Schwarz, Jonathan and Lillicrap, Timothy P and Wayne, Greg)", "Selective Experience Replay for Lifelong Learning (Isele, David and Cosgun, Akansel)", "Continual Reinforcement Learning with Complex Synapses (Kaplanis, Christos and Shanahan, Murray and Clopath, Claudia)", "Unicorn: Continual Learning with a Universal, Off-policy Agent (Mankowitz, Daniel J and \u017d\u00eddek, Augustin and Barreto, Andr\u00e9 and Horgan, Dan and Hessel, Matteo and Quan, John and Oh, Junhyuk and van Hasselt, Hado and Silver, David and Schaul, Tom)", "Lifelong Inverse Reinforcement Learning (Mendez, Jorge A and Shivkumar, Shashank and Eaton, Eric)", "Progress & Compress: A Scalable Framework for Continual Learning (Schwarz, Jonathan and Czarnecki, Wojciech and Luketina, Jelena and Grabska-Barwinska, Agnieszka and Teh, Yee Whye and Pascanu, Razvan and Hadsell, Raia)", "Overcoming Catastrophic Forgetting in Neural Networks (Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia)", "Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory (L\u00fcders, Benno and Schl\u00e4ger, Mikkel and Korach, Aleksandra and Risi, Sebastian)", "Stable Predictive Representations with General Value Functions for Continual Learning (Schlegel, Matthew and White, Adam and White, Martha)", "Continual Learning through Evolvable Neural Turing Machines (Luders, Benno and Schlager, Mikkel and Risi, Sebastian)", "Progressive Neural Networks (Rusu, Andrei A and Rabinowitz, Neil C and Desjardins, Guillaume and Soyer, Hubert and Kirkpatrick, James and Kavukcuoglu, Koray and Pascanu, Razvan and Hadsell, Raia)", "Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets. (Shu, Lei and Liu, Bing and Xu, Hu and Kim, Annice)", "CHILD: A First Step Towards Continual Learning (Ring, Mark B)", "Continual Sequence Generation with Adaptive Compositional Modules (Zhang, Yanzhe and Wang, Xuezhi and Yang, Diyi)", "Continual Learning for Recurrent Neural Networks: An Empirical Evaluation (Cossu, Andrea and Carta, Antonio and Lomonaco, Vincenzo and Bacciu, Davide)", "Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning (Ororbia, Alexander G.)", "Continual Learning with Gated Incremental Memories for Sequential Data Processing (Cossu, Andrea and Carta, Antonio and Bacciu, Davide)", "Organizing Recurrent Network Dynamics by Task-Computation to Enable Continual Learning (Duncker, Lea and Driscoll, Laura N and Shenoy, Krishna V and Sahani, Maneesh and Sussillo, David)", "Meta-Consolidation for Continual Learning (Joseph, K J and Balasubramanian, Vineeth N)", "Compositional Language Continual Learning (Li, Yuanpeng and Zhao, Liang and Church, Kenneth and Elhoseiny, Mohamed)", "Online Continual Learning on Sequences (Parisi, German I and Lomonaco, Vincenzo)", "Unsupervised Progressive Learning and the STAM Architecture (Smith, James and Baer, Seth and Taylor, Cameron and Dovrolis, Constantine)", "Toward Training Recurrent Neural Networks for Lifelong Learning (Sodhani, Shagun and Chandar, Sarath and Bengio, Yoshua)", "Semi-Supervised Tuning from Temporal Coherence (Maltoni, Davide and Lomonaco, Vincenzo)", "Self-Refreshing Memory in Artificial Neural Networks: Learning Temporal Sequences without Catastrophic Forgetting (Ans, Bernard and Rousset, St\u00e9phane and French, Robert M. and Musca, Serban)", "Using Pseudo-Recurrent Connectionist Networks to Solve the Problem of Sequential Learning (French, Robert)", "Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes (Lesort, Timoth'ee)", "Continual Learning in Neural Networks (Aljundi, Rahaf)", "Continual Deep Learning via Progressive Learning (Fayek, Haytham M.)", "Continual Learning with Deep Architectures (Lomonaco, Vincenzo)", "Explanation-Based Neural Network Learning: A Lifelong Learning Approach (Thrun, Sebastian)", "Continual Learning in Reinforcement Environments (Ring, Mark)", "Foundational Models for Continual Learning: An Empirical Study of Latent Replay (Ostapenko, Oleksiy and Lesort, Timothee and Rodr\u00edguez, Pau and Arefin, Md Rifat and Douillard, Arthur and Rish, Irina and Charlin, Laurent)", "Brain-Inspired Replay for Continual Learning with Artificial Neural Networks (van de Ven, Gido M. and Siegelmann, Hava T. and Tolias, Andreas S.)", "Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (Rostami, Mohammad and Kolouri, Soheil and Pilly, Praveen K)", "Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (Rostami, Mohammad and Kolouri, Soheil and Pilly, Praveen K.)", "Continual Learning of New Sound Classes Using Generative Replay (Wang, Zhepei and Subakan, Cem and Tzinis, Efthymios and Smaragdis, Paris and Charlin, Laurent)", "Generative Replay with Feedback Connections as a General Strategy for Continual Learning (van de Ven, Gido M. and Tolias, Andreas S.)", "Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory (L\u00fcders, Benno and Schl\u00e4ger, Mikkel and Korach, Aleksandra and Risi, Sebastian)", "Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches (Lomonaco, Vincenzo and Maltoni, Davide and Pellegrini, Lorenzo)", "Linear Mode Connectivity in Multitask and Continual Learning (Mirzadeh, Seyed Iman and Farajtabar, Mehrdad and Gorur, Dilan and Pascanu, Razvan and Ghasemzadeh, Hassan)", "Efficient Continual Learning in Neural Networks with Embedding Regularization (Pomponi, Jary and Scardapane, Simone and Lomonaco, Vincenzo and Uncini, Aurelio)", "Efficient Lifelong Learning with A-GEM (Chaudhry, Arslan and Ranzato, Marc'Aurelio and Rohrbach, Marcus and Elhoseiny, Mohamed)", "Single-Net Continual Learning with Progressive Segmented Training (PST) (Du, Xiaocong and Charan, Gouranga and Liu, Frank and Cao, Yu)", "Continuous Learning in Single-Incremental-Task Scenarios (Maltoni, Davide and Lomonaco, Vincenzo)", "Toward Training Recurrent Neural Networks for Lifelong Learning (Sodhani, Shagun and Chandar, Sarath and Bengio, Yoshua)", "Continual Learning of New Sound Classes Using Generative Replay (Wang, Zhepei and Subakan, Cem and Tzinis, Efthymios and Smaragdis, Paris and Charlin, Laurent)", "Lifelong Learning via Progressive Distillation and Retrospection (Hou, Saihui and Pan, Xinyu and Loy, Chen Change and Wang, Zilei and Lin, Dahua)", "Progress & Compress: A Scalable Framework for Continual Learning (Schwarz, Jonathan and Czarnecki, Wojciech and Luketina, Jelena and Grabska-Barwinska, Agnieszka and Teh, Yee Whye and Pascanu, Razvan and Hadsell, Raia)", "Gradient Episodic Memory for Continual Learning (Lopez-Paz, David and Ranzato, Marc'Aurelio)", "Learning to Continually Learn (Beaulieu, Shawn and Frati, Lapo and Miconi, Thomas and Lehman, Joel and Stanley, Kenneth O. and Clune, Jeff and Cheney, Nick)", "Continual Learning with Deep Artificial Neurons (Camp, Blake and Mandivarapu, Jaya Krishna and Estrada, Rolando)", "Meta-Consolidation for Continual Learning (Joseph, K J and Balasubramanian, Vineeth N)", "Meta Continual Learning via Dynamic Programming (Krishnan, R and Balaprakash, Prasanna)", "Online Meta-Learning (Finn, Chelsea and Rajeswaran, Aravind and Kakade, Sham and Levine, Sergey)", "Task Agnostic Continual Learning via Meta Learning (He, Xu and Sygnowski, Jakub and Galashov, Alexandre and Rusu, Andrei A and Teh, Yee Whye and Pascanu, Razvan)", "Meta-Learning Representations for Continual Learning (Javed, Khurram and White, Martha)", "Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference (Riemer, Matthew and Cases, Ignacio and Ajemian, Robert and Liu, Miao and Rish, Irina and Tu, Yuhai and Tesauro, Gerald)", "Meta Continual Learning (Vuorio, Risto and Cho, Dong-Yeon and Kim, Daejoong and Kim, Jiwon)", "Continual Learning in Deep Networks: An Analysis of the Last Layer (Lesort, Timoth\u00e9e and George, Thomas and Rish, Irina)", "Avalanche: An End-to-End Library for Continual Learning (Lomonaco, Vincenzo and Pellegrini, Lorenzo and Cossu, Andrea and Carta, Antonio and Graffieti, Gabriele and Hayes, Tyler L. and De Lange, Matthias and Masana, Marc and Pomponi, Jary and van de Ven, Gido and Mundt, Martin and She, Qi and Cooper, Keiland and Forest, Jeremy and Belouadah, Eden and Calderara, Simone and Parisi, German I. and Cuzzolin, Fabio and Tolias, Andreas and Scardapane, Simone and Antiga, Luca and Amhad, Subutai and Popescu, Adrian and Kanan, Christopher and van de Weijer, Joost and Tuytelaars, Tinne and Bacciu, Davide and Maltoni, Davide)", "CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability (Mundt, Martin and Lang, Steven and Delfosse, Quentin and Kersting, Kristian)", "Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning (Caccia, Massimo and Rodriguez, Pau and Ostapenko, Oleksiy and Normandin, Fabrice and Lin, Min and Caccia, Lucas and Laradji, Issam and Rish, Irina and Lacoste, Alexande and Vazquez, David and Charlin, Laurent)", "Optimal Continual Learning Has Perfect Memory and Is NP-HARD (Knoblauch, Jeremias and Husain, Hisham and Diethe, Tom)", "Regularization Shortcomings for Continual Learning (Lesort, Timoth\u00e9e and Stoian, Andrei and Filliat, David)", "Strategies for Improving Single-Head Continual Learning Performance (El Khatib, Alaa and Karray, Fakhri)", "Towards Robust Evaluations of Continual Learning (Farquhar, Sebastian and Gal, Yarin)", "Don't Forget, There Is More than Forgetting: New Metrics for Continual Learning (D\u00edaz-Rodr\\\u01f5uez, Natalia and Lomonaco, Vincenzo and Filliat, David and Maltoni, Davide)", "Three Scenarios for Continual Learning (van de Ven, Gido M and Tolias, Andreas S)", "Biological Underpinnings for Lifelong Learning Machines (Kudithipudi, Dhireesha and Aguilar-Simon, Mario and Babb, Jonathan and Bazhenov, Maxim and Blackiston, Douglas and Bongard, Josh and Brna, Andrew P. and Chakravarthi Raja, Suraj and Cheney, Nick and Clune, Jeff and Daram, Anurag and Fusi, Stefano and Helfer, Peter and Kay, Leslie and Ketz, Nicholas and Kira, Zsolt and Kolouri, Soheil and Krichmar, Jeffrey L. and Kriegman, Sam and Levin, Michael and Madireddy, Sandeep and Manicka, Santosh and Marjaninejad, Ali and McNaughton, Bruce and Miikkulainen, Risto and Navratilova, Zaneta and Pandit, Tej and Parker, Alice and Pilly, Praveen K. and Risi, Sebastian and Sejnowski, Terrence J. and Soltoggio, Andrea and Soures, Nicholas and Tolias, Andreas S. and Urbina-Mel\u00e9ndez, Dar\u00edo and Valero-Cuevas, Francisco J. and van de Ven, Gido M. and Vogelstein, Joshua T. and Wang, Felix and Weiss, Ron and Yanguas-Gil, Angel and Zou, Xinyun and Siegelmann, Hava)", "Neural Inhibition for Continual Learning and Memory (Barron, Helen C)", "Can Sleep Protect Memories from Catastrophic Forgetting? (Gonzalez, Oscar C and Sokolov, Yury and Krishnan, Giri and Bazhenov, Maxim)", "Synaptic Consolidation: An Approach to Long-Term Learning (Clopath, Claudia)", "The Organization of Behavior: A Neuropsychological Theory (Hebb, D O)", "Negative Transfer Errors in Sequential Cognitive Skills: Strong-but-wrong Sequence Application. (Woltz, Dan J. and Gardner, Michael K. and Bell, Brian G.)", "Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions. (Ratcliff, R)", "Continual Novelty Detection (Aljundi, Rahaf and Reino, Daniel Olmeda and Chumerin, Nikolay and Turner, Richard E.)", "Co\\$2\u0302\\$L: Contrastive Continual Learning (Cha, Hyuntak and Lee, Jaeho and Shin, Jinwoo)", "Sustainable Artificial Intelligence through Continual Learning (Cossu, Andrea and Ziosi, Marta and Lomonaco, Vincenzo)", "Continual Backprop: Stochastic Gradient Descent with Persistent Randomness (Dohare, Shibhansh and Mahmood, A. Rupam and Sutton, Richard S.)", "Continuum: Simple Management of Complex Continual Learning Scenarios (Douillard, Arthur and Lesort, Timoth\u00e9e)", "Posterior Meta-Replay for Continual Learning (Henning, Christian and Cervera, Maria and D'Angelo, Francesco and Oswald, Johannes Von and Traber, Regina and Ehret, Benjamin and Kobayashi, Seijin and Grewe, Benjamin F. and Sacramento, Joao)", "Rethinking the Representational Continuity: Towards Unsupervised Continual Learning (Madaan, Divyam and Yoon, Jaehong and Li, Yuanchun and Liu, Yunxin and Hwang, Sung Ju)", "Representation Memorization for Fast Learning New Knowledge without Forgetting (Mi, Fei and Lin, Tao and Faltings, Boi)", "Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning (Chen, Tianlong and Zhang, Zhenyu and Liu, Sijia and Chang, Shiyu and Wang, Zhangyang)", "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (Hayes, Tyler L and Kanan, Christopher)", "Continual Learning with Bayesian Neural Networks for Non-Stationary Data (Kurle, Richard and Cseke, Botond and Klushyn, Alexej and van der Smagt, Patrick and G\u00fcnnemann, Stephan)", "Continual Learning Using Task Conditional Neural Networks (Li, Honglin and Barnaghi, Payam and Enshaeifar, Shirin and Ganz, Frieder)", "Energy-Based Models for Continual Learning (Li, Shuang and Du, Yilun and van de Ven, Gido M. and Torralba, Antonio and Mordatch, Igor)", "Continual Universal Object Detection (Liu, Xialei and Yang, Hao and Ravichandran, Avinash and Bhotika, Rahul and Soatto, Stefano)", "Mnemonics Training: Multi-Class Incremental Learning without Forgetting (Liu, Yaoyao and Liu, An-An and Su, Yuting and Schiele, Bernt and Sun, Qianru)", "Structured Compression and Sharing of Representational Space for Continual Learning (Saha, Gobinda and Garg, Isha and Ankit, Aayush and Roy, Kaushik)", "Gradient Projection Memory for Continual Learning (Saha, Gobinda and Roy, Kaushik)", "Gated Linear Networks (Veness, Joel and Lattimore, Tor and Budden, David and Bhoopchand, Avishkar and Mattern, Christopher and Grabska-Barwinska, Agnieszka and Sezener, Eren and Wang, Jianan and Toth, Peter and Schmitt, Simon and Hutter, Marcus)", "Lifelong Graph Learning (Wang, Chen and Qiu, Yuheng and Scherer, Sebastian)", "Superposition of Many Models into One (Cheung, Brian and Terekhov, Alex and Chen, Yubei and Agrawal, Pulkit and Olshausen, Bruno)", "Continual Learning in Practice (Diethe, Tom and Borchert, Tom and Thereska, Eno and Balle, Borja and Lawrence, Neil)", "Dynamically Constraining Connectionist Networks to Produce Distributed, Orthogonal Representations to Reduce Catastrophic Interference (French, Robert)", "Continual Learning via Neural Pruning (Golkar, Siavash and Kagan, Michael and Cho, Kyunghyun)", "BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach (Kuzina, Anna and Egorov, Evgenii and Burnaev, Evgeny)", "Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild (Lee, Kibok and Lee, Kimin and Shin, Jinwoo and Lee, Honglak)", "Continual Learning Using Bayesian Neural Networks (Li, HongLin and Barnaghi, Payam and Enshaeifar, Shirin and Ganz, Frieder)", "Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition (Mundt, Martin and Majumder, Sagnik and Pliushch, Iuliia and Hong, Yong Won and Ramesh, Visvanathan)", "Continual Rare-Class Recognition with Emerging Novel Subclasses (Nguyen, Hung and Wang, Xuejian and Akoglu, Leman)", "Random Path Selection for Incremental Learning (Rajasegaran, Jathushan and Hayat, Munawar and Fahad, Salman Khan and Khan, Shahbaz and Shao, Ling)", "Improving and Understanding Variational Continual Learning (Swaroop, Siddharth and Nguyen, Cuong V and Bui, Thang D and Turner, Richard E)", "Continual Learning via Online Leverage Score Sampling (Teng, Dan and Dasgupta, Sakyasingha)", "Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class Classifiers (Ye, Xin and Zhu, Qiuyu)", "Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies (Achille, Alessandro and Eccles, Tom and Matthey, Loic and Burgess, Christopher P. and Watters, Nick and Lerchner, Alexander and Higgins, Irina)", "Overcoming Catastrophic Interference Using Conceptor-Aided Backpropagation (He, Xu and Jaeger, Herbert)", "Less-Forgetful Learning for Domain Expansion in Deep Neural Networks (Jung, Heechul and Ju, Jeongwoo and Jung, Minju and Kim, Junmo)", "Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (Mallya, Arun and Davis, Dillon and Lazebnik, Svetlana)", "Adding New Tasks to a Single Network with Weight Transformations Using Binary Masks (Mancini, Massimiliano and Ricci, Elisa and Caputo, Barbara and Bul\u00f2, Samuel Rota)", "Variational Continual Learning (Nguyen, Cuong V and Li, Yingzhen and Bui, Thang D and Turner, Richard E)", "Task Agnostic Continual Learning Using Online Variational Bayes (Zeno, Chen and Golan, Itay and Hoffer, Elad and Soudry, Daniel)", "Encoder Based Lifelong Learning (Triki, Amal Rannen and Aljundi, Rahaf and Blaschko, Mathew B. and Tuytelaars, Tinne)", "Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios (K\u00e4ding, Christoph and Rodner, Erik and Freytag, Alexander and Denzler, Joachim)", "Using Hindsight to Anchor Past Knowledge in Continual Learning (Chaudhry, Arslan and Gordo, Albert and Dokania, Puneet K. and Torr, Philip and Lopez-Paz, David)", "Contrastive Continual Learning with Feature Propagation (Han, Xuejun and Guo, Yuhong)", "Gradient Projection Memory for Continual Learning (Saha, Gobinda and Garg, Isha and Roy, Kaushik)", "Modeling the Background for Incremental Learning in Semantic Segmentation (Cermelli, Fabio and Mancini, Massimiliano and Bul\u00f2, Samuel Rota and Ricci, Elisa and Caputo, Barbara)", "PLOP: Learning without Forgetting for Continual Semantic Segmentation (Douillard, Arthur and Chen, Yifu and Dapogny, Arnaud and Cord, Matthieu)", "Insights from the Future for Continual Learning (Douillard, Arthur and Valle, Eduardo and Ollion, Charles and Robert, Thomas and Cord, Matthieu)", "PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (Douillard, Arthur and Cord, Matthieu and Ollion, Charles and Robert, Thomas and Valle, Eduardo)", "Uncertainty-Guided Continual Learning with Bayesian Neural Networks (Ebrahimi, Sayna and Elhoseiny, Mohamed and Darrell, Trevor and Rohrbach, Marcus)", "Continual Learning of Object Instances (Parshotam, Kishan and Kilickaya, Mert)", "Efficient Continual Learning in Neural Networks with Embedding Regularization (Pomponi, Jary and Scardapane, Simone and Lomonaco, Vincenzo and Uncini, Aurelio)", "Continual Learning with Hypernetworks (von Oswald, Johannes and Henning, Christian and Sacramento, Jo\u00e3o and Grewe, Benjamin F)", "Uncertainty-Based Continual Learning with Adaptive Regularization (Ahn, Hongjoon and Cha, Sungmin and Lee, Donggyu and Moon, Taesup)", "Learning without Memorizing (Dhar, Prithviraj and Vikram Singh, Rajat and Peng, Kuan-Chuan and Wu, Ziyan and Chellappa, Rama)", "Incremental Learning Techniques for Semantic Segmentation (Michieli, Umberto and Zanuttigh, Pietro)", "Functional Regularisation for Continual Learning Using Gaussian Processes (Titsias, Michalis K and Schwarz, Jonathan and Matthews, Alexander G de G and Pascanu, Razvan and Teh, Yee Whye)", "Rotate Your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (Liu, Xialei and Masana, Marc and Herranz, Luis and Van de Weijer, Joost and Lopez, Antonio M. and Bagdanov, Andrew D)", "Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting (Ritter, Hippolyt and Botev, Aleksandar and Barber, David)", "Overcoming Catastrophic Forgetting with Hard Attention to the Task (Serr\u00e0, Joan and Sur\u00eds, D\\d\u0301ac and Miron, Marius and Karatzoglou, Alexandros)", "Overcoming Catastrophic Forgetting in Neural Networks (Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia)", "Overcoming Catastrophic Forgetting by Incremental Moment Matching (Lee, Sang-Woo and Kim, Jin-Hwa and Jun, Jaehyun and Ha, Jung-Woo and Zhang, Byoung-Tak)", "Lifelong Generative Modeling (Ramapuram, Jason and Gregorova, Magda and Kalousis, Alexandros)", "Continual Learning in Generative Adversarial Nets (Seff, Ari and Beatson, Alex and Suo, Daniel and Liu, Han)", "Incremental Learning of Object Detectors without Catastrophic Forgetting (Shmelkov, Konstantin and Schmid, Cordelia and Alahari, Karteek)", "Continual Learning Through Synaptic Intelligence (Zenke, Friedemann and Poole, Ben and Ganguli, Surya)", "Learning without Forgetting (Li, Zhizhong and Hoiem, Derek)", "Foundational Models for Continual Learning: An Empirical Study of Latent Replay (Ostapenko, Oleksiy and Lesort, Timothee and Rodr\u00edguez, Pau and Arefin, Md Rifat and Douillard, Arthur and Rish, Irina and Charlin, Laurent)", "Using Hindsight to Anchor Past Knowledge in Continual Learning (Chaudhry, Arslan and Gordo, Albert and Dokania, Puneet K. and Torr, Philip and Lopez-Paz, David)", "Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams (De Lange, Matthias and Tuytelaars, Tinne)", "Replay in Deep Learning: Current Approaches and Missing Biological Elements (Hayes, Tyler L. and Krishnan, Giri P. and Bazhenov, Maxim and Siegelmann, Hava T. and Sejnowski, Terrence J. and Kanan, Christopher)", "Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay -- 3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A Continual Object Classification (Kurniawan, Muhammad Rifki and Wei, Xing and Gong, Yihong)", "Distilled Replay: Overcoming Forgetting through Synthetic Samples (Rosasco, Andrea and Carta, Antonio and Cossu, Andrea and Lomonaco, Vincenzo and Bacciu, Davide)", "Online Coreset Selection for Rehearsal-based Continual Learning (Yoon, Jaehong and Madaan, Divyam and Yang, Eunho and Hwang, Sung Ju)", "CALM: Continuous Adaptive Learning for Language Modeling (Arumae, Kristjan and Bhatia, Parminder)", "REMIND Your Neural Network to Prevent Catastrophic Forgetting (Hayes, Tyler L. and Kafle, Kushal and Shrestha, Robik and Acharya, Manoj and Kanan, Christopher)", "CLOPS: Continual Learning of Physiological Signals (Kiyasseh, Dani and Zhu, Tingting and Clifton, David A)", "Continual Learning with Bayesian Neural Networks for Non-Stationary Data (Kurle, Richard and Cseke, Botond and Klushyn, Alexej and van der Smagt, Patrick and G\u00fcnnemann, Stephan)", "GDumb: A Simple Approach That Questions Our Progress in Continual Learning (Prabhu, Ameya and Torr, Philip H. S. and Dokania, Puneet K.)", "Graph-Based Continual Learning (Tang, Binh and Matteson, David S.)", "Brain-Inspired Replay for Continual Learning with Artificial Neural Networks (van de Ven, Gido M. and Siegelmann, Hava T. and Tolias, Andreas S.)", "Continual Learning with Hypernetworks (von Oswald, Johannes and Henning, Christian and Sacramento, Jo\u00e3o and Grewe, Benjamin F)", "Online Continual Learning with Maximal Interfered Retrieval (Aljundi, Rahaf and Belilovsky, Eugene and Tuytelaars, Tinne and Charlin, Laurent and Caccia, Massimo and Lin, Min and Page-Caccia, Lucas)", "On Tiny Episodic Memories in Continual Learning (Chaudhry, Arslan and Rohrbach, Marcus and Elhoseiny, Mohamed and Ajanthan, Thalaiyasingam and Dokania, Puneet K and Torr, Philip H S and Ranzato, Marc'Aurelio)", "Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients (Chen, Yu and Diethe, Tom and Lawrence, Neil)", "Memory Efficient Experience Replay for Streaming Learning (Hayes, Tyler L and Cahill, Nathan D and Kanan, Christopher)", "Experience Replay for Continual Learning (Rolnick, David and Ahuja, Arun and Schwarz, Jonathan and Lillicrap, Timothy P and Wayne, Greg)", "Prototype Reminding for Continual Learning (Zhang, Mengmi and Wang, Tao and Lim, Joo Hwee and Feng, Jiashi)", "Selective Experience Replay for Lifelong Learning (Isele, David and Cosgun, Akansel)", "Preventing Catastrophic Interference in MultipleSequence Learning Using Coupled Reverberating Elman Networks (Ans, Bernard and Rousset, Stephane and French, Robert M. and Musca, Serban C.)", "Continual Learning for Recurrent Neural Networks: An Empirical Evaluation (Cossu, Andrea and Carta, Antonio and Lomonaco, Vincenzo and Bacciu, Davide)", "A Continual Learning Survey: Defying Forgetting in Classification Tasks (De Lange, Matthias and Aljundi, Rahaf and Masana, Marc and Parisot, Sarah and Jia, Xu and Leonardis, Ales and Slabaugh, Gregory and Tuytelaars, Tinne)", "Replay in Deep Learning: Current Approaches and Missing Biological Elements (Hayes, Tyler L. and Krishnan, Giri P. and Bazhenov, Maxim and Siegelmann, Hava T. and Sejnowski, Terrence J. and Kanan, Christopher)", "Continual Lifelong Learning in Natural Language Processing: A Survey (Biesialska, Magdalena and Biesialska, Katarzyna and Costa-juss\u00e0, Marta R.)", "Embracing Change: Continual Learning in Deep Neural Networks (Hadsell, Raia and Rao, Dushyant and Rusu, Andrei A and Pascanu, Razvan)", "Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges (Lesort, Timoth\u00e9e and Lomonaco, Vincenzo and Stoian, Andrei and Maltoni, Davide and Filliat, David and D\u00edaz-Rodr\\\u01f5uez, Natalia)", "A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning (Mundt, Martin and Hong, Yong Won and Pliushch, Iuliia and Ramesh, Visvanathan)", "A Review of Off-Line Mode Dataset Shifts (Takahashi, Carla C. and Braga, Antonio P.)", "Continual Learning with Neural Networks: A Review (Awasthi, Abhijeet and Sarawagi, Sunita)", "Continual Lifelong Learning with Neural Networks: A Review (Parisi, German I and Kemker, Ronald and Part, Jose L and Kanan, Christopher and Wermter, Stefan)", "Lifelong Machine Learning, Second Edition (Chen, Zhiyuan and Liu, Bing)", "Measuring Catastrophic Forgetting in Neural Networks (Kemker, Ronald and McClure, Marc and Abitino, Angelina and Hayes, Tyler L and Kanan, Christopher)", "Generative Models from the Perspective of Continual Learning (Lesort, Timoth\u00e9e and Caselles-Dupr\u00e9, Hugo and Garcia-Ortiz, Michael and Stoian, Andrei and Filliat, David)", "Incremental On-Line Learning: A Review and Comparison of State of the Art Algorithms (Losing, Viktor and Hammer, Barbara and Wersing, Heiko)", "A Comprehensive, Application-Oriented Study of Catastrophic Forgetting in DNNs (Pf\u00fclb, B and Gepperth, A)", "Born to Learn: The Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (Soltoggio, Andrea and Stanley, Kenneth O. and Risi, Sebastian)", "Avoiding Catastrophic Forgetting (Hasselmo, Michael E.)", "Learning in Nonstationary Environments: A Survey (Ditzler, Gregory and Roveri, Manuel and Alippi, Cesare and Polikar, Robi)", "Never-Ending Learning (Mitchell, Tom and Cohen, William W and Hruschka, E and Talukdar, Partha P and Yang, B and Betteridge, Justin and Carlson, Andrew and Dalvi, B and Gardner, Matt and Kisiel, Bryan and Krishnamurthy, J and Lao, Ni and Mazaitis, K and Mohamed, T and Nakashole, N and Platanios, E and Ritter, A and Samadi, M and Settles, B and Wang, R and Wijaya, D and Gupta, A and Chen, X and Saparov, A and Greaves, M and Welling, J)", "Catastrophic Forgetting; Catastrophic Interference; Stability; Plasticity; Rehearsal. (Robins, Anthony)", "Online Continual Learning for Embedded Devices (Hayes, Tyler L. and Kanan, Christopher)", "Controlling Soft Robotic Arms Using Continual Learning (Piqu\u00e9, Francesco and Kalidindi, Hari Teja and Fruzzetti, Lorenzo and Laschi, Cecilia and Menciassi, Arianna and Falotico, Egidio)", "Tell Me What This Is: Few-Shot Incremental Object Learning by a Robot (Ayub, Ali and Wagner, Alan R.)", "Online Object and Task Learning via Human Robot Interaction (Dehghan, M. and Zhang, Z. and Siam, M. and Jin, J. and Petrich, L. and Jagersand, M.)", "Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics (Wong, Jay M)", "A Lifelong Learning Perspective for Mobile Robot Control (Thrun, Sebastian)", "Explanation-Based Neural Network Learning for Robot Control (Mitchell, Tom M and Thrun, Sebastian B)"], "sec_title": ["0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "1", "1", "1", "1", "1", "1", "1", 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Baydoun, Mohamad and Marcenaro, Lucio and Regazzoni, Carlo)", "Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem (De Lange, Matthias and Jia, Xu and Parisot, Sarah and Leonardis, Ales and Slabaugh, Gregory and Tuytelaars, Tinne)", "Incremental Learning for End-to-End Automatic Speech Recognition (Fu, Li and Li, Xiaoxiao and Zi, Libo)", "Neural Topic Modeling with Continual Lifelong Learning (Gupta, Pankaj and Chaudhary, Yatin and Runkler, Thomas and Sch\u00fctze, Hinrich)", "CLOPS: Continual Learning of Physiological Signals (Kiyasseh, Dani and Zhu, Tingting and Clifton, David A)", "Continual Learning for Domain Adaptation in Chest X-ray Classification (Lenga, Matthias and Schulz, Heinrich and Saalbach, Axel)", "Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis (Madasu, Avinash and Rao, Vijjini Anvesh)", "Importance Driven Continual Learning for Segmentation Across Domains (\u00d6zg\u00fcn, Sinan \u00d6zg\u00fcr and Rickmann, Anne-Marie and Roy, Abhijit Guha and Wachinger, Christian)", "LAMOL: LAnguage MOdeling for Lifelong Language Learning (Sun, Fan-Keng and Ho, Cheng-Hao and Lee, Hung-Yi)", "Non-Parametric Adaptation for Neural Machine Translation (Bapna, Ankur and Firat, Orhan)", "Episodic Memory in Lifelong Language Learning (D'Autume, Cyprien de Masson and Ruder, Sebastian and Kong, Lingpeng and Yogatama, Dani)", "Continual Adaptation for Efficient Machine Communication (Hawkins, Robert D and Kwon, Minae and Sadigh, Dorsa and Goodman, Noah D)", "Continual Learning for Sentence Representations Using Conceptors (Liu, Tianlin and Ungar, Lyle and Sedoc, Jo\u00e3o)", "Lifelong and Interactive Learning of Factual Knowledge in Dialogues (Mazumder, Sahisnu and Liu, Bing and Wang, Shuai and Ma, Nianzu)", "Making Good on LSTMs' Unfulfilled Promise (Philps, Daniel and d'Avila Garcez, Artur and Weyde, Tillman)", "Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation (Thompson, Brian and Gwinnup, Jeremy and Khayrallah, Huda and Duh, Kevin and Koehn, Philipp)", "Lifelong Learning for Scene Recognition in Remote Sensing Images (Zhai, Min and Liu, Huaping and Sun, Fuchun)", "Towards Continual Learning in Medical Imaging (Baweja, Chaitanya and Glocker, Ben and Kamnitsas, Konstantinos)", "Toward Continual Learning for Conversational Agents (Lee, Sungjin)", "Toward an Architecture for Never-Ending Language Learning (Carlson, Andrew and Betteridge, Justin and Kisiel, Bryan and Settles, Burr and Hruschka, Estevam R. and Mitchell, Tom M.)", "Provable and Efficient Continual Representation Learning (Li, Yingcong and Li, Mingchen and Asif, M. Salman and Oymak, Samet)", "Architecture Matters in Continual Learning (Mirzadeh, Seyed Iman and Chaudhry, Arslan and Yin, Dong and Nguyen, Timothy and Pascanu, Razvan and Gorur, Dilan and Farajtabar, Mehrdad)", "Continual Learning with Node-Importance Based Adaptive Group Sparse Regularization (Jung, Sangwon and Ahn, Hongjoon and Cha, Sungmin and Moon, Taesup)", "Structured Ensembles: An Approach to Reduce the Memory Footprint of Ensemble Methods (Pomponi, Jary and Scardapane, Simone and Uncini, Aurelio)", "Modular Dynamic Neural Network: A Continual Learning Architecture (Turner, Daniel and Cardoso, Pedro J. S. and Rodrigues, Jo\u00e3o M. F.)", "Continual Learning with Adaptive Weights (CLAW) (Adel, Tameem and Zhao, Han and Turner, Richard E)", "Continual Learning with Gated Incremental Memories for Sequential Data Processing (Cossu, Andrea and Carta, Antonio and Bacciu, Davide)", "Continual Learning in Recurrent Neural Networks (Ehret, Benjamin and Henning, Christian and Cervera, Maria and Meulemans, Alexander and Oswald, Johannes Von and Grewe, Benjamin F.)", "Explainability in Deep Reinforcement Learning (Heuillet, Alexandre and Couthouis, Fabien and D\u00edaz-Rodr\\\u01f5uez, Natalia)", "Bayesian Nonparametric Weight Factorization for Continual Learning (Mehta, Nikhil and Liang, Kevin J and Carin, Lawrence)", "SpaceNet: Make Free Space For Continual Learning (Sokar, Ghada and Mocanu, Decebal Constantin and Pechenizkiy, Mykola)", "Efficient Continual Learning with Modular Networks and Task-Driven Priors (Veniat, Tom and Denoyer, Ludovic and Ranzato, Marc'Aurelio)", "Progressive Memory Banks for Incremental Domain Adaptation (Asghar, Nabiha and Mou, Lili and Selby, Kira A and Pantasdo, Kevin D and Poupart, Pascal and Jiang, Xin)", "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments (Ashfahani, Andri and Pratama, Mahardhika)", "Compacting, Picking and Growing for Unforgetting Continual Learning (Hung, Steven C Y and Tu, Cheng-Hao and Wu, Cheng-En and Chen, Chien-Hung and Chan, Yi-Ming and Chen, Chu-Song)", "Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (Li, Xilai and Zhou, Yingbo and Wu, Tianfu and Socher, Richard and Xiong, Caiming)", "Towards AutoML in the Presence of Drift: First Results (Madrid, Jorge G. and Escalante, Hugo Jair and Morales, Eduardo F. and Tu, Wei-Wei and Yu, Yang and Sun-Hosoya, Lisheng and Guyon, Isabelle and Sebag, Michele)", "Continual Unsupervised Representation Learning (Rao, Dushyant and Visin, Francesco and Rusu, Andrei A and Teh, Yee Whye and Pascanu, Razvan and Hadsell, Raia)", "A Progressive Model to Enable Continual Learning for Semantic Slot Filling (Shen, Yilin and Zeng, Xiangyu and Jin, Hongxia)", "Adaptive Compression-based Lifelong Learning (Srivastava, Shivangi and Berman, Maxim and Blaschko, Matthew B and Tuia, Devis)", "Frosting Weights for Better Continual Training (Zhu, Xiaofeng and Liu, Feng and Trajcevski, Goce and Wang, Dingding)", "Dynamic Few-Shot Visual Learning Without Forgetting (Gidaris, Spyros and Komodakis, Nikos)", "HOUDINI: Lifelong Learning as Program Synthesis (Valkov, Lazar and Chaudhari, Dipak and Srivastava, Akash and Sutton, Charles and Chaudhuri, Swarat)", "Reinforced Continual Learning (Xu, Ju and Zhu, Zhanxing)", "Lifelong Learning With Dynamically Expandable Networks (Yoon, Jaehong and Yang, Eunho and Lee, Jeongtae and Hwang, Sung Ju)", "Expert Gate: Lifelong Learning with a Network of Experts (Aljundi, Rahaf and Chakravarty, Punarjay and Tuytelaars, Tinne)", "Neurogenesis Deep Learning (Draelos, Timothy John and Miner, Nadine E and Lamb, Christopher and Cox, Jonathan A and Vineyard, Craig Michael and Carlson, Kristofor David and Severa, William Mark and James, Conrad D and Aimone, James Bradley)", "Net2Net: Accelerating Learning via Knowledge Transfer (Chen, Tianqi and Goodfellow, Ian and Shlens, Jonathon)", "Continual Learning through Evolvable Neural Turing Machines (Luders, Benno and Schlager, Mikkel and Risi, Sebastian)", "Progressive Neural Networks (Rusu, Andrei A and Rabinowitz, Neil C and Desjardins, Guillaume and Soyer, Hubert and Kirkpatrick, James and Kavukcuoglu, Koray and Pascanu, Razvan and Hadsell, Raia)", "Knowledge Transfer in Deep Block-Modular Neural Networks (Terekhov, Alexander V. and Montone, Guglielmo and O'Regan, J. Kevin)", "ELLA: An Efficient Lifelong Learning Algorithm (Ruvolo, Paul and Eaton, Eric)", "A Self-Organising Network That Grows When Required (Marsland, Stephen and Shapiro, Jonathan and Nehmzow, Ulrich)", "vCLIMB: A Novel Video Class Incremental Learning Benchmark (Villa, Andr\u00e9s and Alhamoud, Kumail and Alc\u00e1zar, Juan Le\u00f3n and Heilbron, Fabian Caba and Escorcia, Victor and Ghanem, Bernard)", "Is Class-Incremental Enough for Continual Learning? (Cossu, Andrea and Graffieti, Gabriele and Pellegrini, Lorenzo and Maltoni, Davide and Bacciu, Davide and Carta, Antonio and Lomonaco, Vincenzo)", "Efficient Continual Learning with Modular Networks and Task-Driven Priors (Veniat, Tom and Denoyer, Ludovic and Ranzato, Marc'Aurelio)", "Defining Benchmarks for Continual Few-Shot Learning (Antoniou, Antreas and Patacchiola, Massimiliano and Ochal, Mateusz and Storkey, Amos)", "Evaluating Online Continual Learning with CALM (Kruszewski, Germ\u00e1n and Sorodoc, Ionut-Teodor and Mikolov, Tomas)", "Continual Reinforcement Learning in 3D Non-Stationary Environments (Lomonaco, Vincenzo and Desai, Karan and Culurciello, Eugenio and Maltoni, Davide)", "OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning (She, Qi and Feng, Fan and Hao, Xinyue and Yang, Qihan and Lan, Chuanlin and Lomonaco, Vincenzo and Shi, Xuesong and Wang, Zhengwei and Guo, Yao and Zhang, Yimin and Qiao, Fei and Chan, Rosa H M)", "New Metrics and Experimental Paradigms for Continual Learning (Hayes, Tyler L. and Kemker, Ronald and Cahill, Nathan D. and Kanan, Christopher)", "CORe50: A New Dataset and Benchmark for Continuous Object Recognition (Lomonaco, Vincenzo and Maltoni, Davide)", "A Biologically Plausible Audio-Visual Integration Model for Continual Learning (Chen, Wenjie and Du, Fengtong and Wang, Ye and Cao, Lihong)", "Synaptic Metaplasticity in Binarized Neural Networks (Laborieux, Axel and Ernoult, Maxence and Hirtzlin, Tifenn and Querlioz, Damien)", "Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks (Allred, Jason M. and Roy, Kaushik)", "Storing Encoded Episodes as Concepts for Continual Learning (Ayub, Ali and Wagner, Alan R.)", "Cognitively-Inspired Model for Incremental Learning Using a Few Examples (Ayub, A. and Wagner, A. R.)", "Spiking Neural Predictive Coding for Continual Learning from Data Streams (Ororbia, Alexander)", "Brain-like Replay for Continual Learning with Artificial Neural Networks (van de Ven, Gido M. and Siegelmann, Hava T. and Tolias, Andreas S.)", "Selfless Sequential Learning (Aljundi, Rahaf and Rohrbach, Marcus and Tuytelaars, Tinne)", "Backpropamine: Training Self-Modifying Neural Networks with Differentiable Neuromodulated Plasticity (Miconi, Thomas and Rawal, Aditya and Clune, Jeff and Stanley, Kenneth O)", "Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations (Ororbia, Alexander and Mali, Ankur and Giles, C Lee and Kifer, Daniel)", "Lifelong Neural Predictive Coding: Sparsity Yields Less Forgetting When Learning Cumulatively (Ororbia, Alexander and Mali, Ankur and Kifer, Daniel and Giles, C Lee)", "FearNet: Brain-Inspired Model for Incremental Learning (Kemker, Ronald and Kanan, Christopher)", "Differentiable Plasticity: Training Plastic Neural Networks with Backpropagation (Miconi, Thomas and Stanley, Kenneth and Clune, Jeff)", "Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (Parisi, German I and Tani, Jun and Weber, Cornelius and Wermter, Stefan)", "Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World (Garg, Sahil and Rish, Irina and Cecchi, Guillermo and Lozano, Aurelie)", "Diffusion-Based Neuromodulation Can Eliminate Catastrophic Forgetting in Simple Neural Networks (Velez, Roby and Clune, Jeff)", "How Do Neurons Operate on Sparse Distributed Representations? A Mathematical Theory of Sparsity, Neurons and Active Dendrites (Ahmad, Subutai and Hawkins, Jeff)", "Continuous Online Sequence Learning with an Unsupervised Neural Network Model (Cui, Yuwei and Ahmad, Subutai and Hawkins, Jeff)", "Mitigation of Catastrophic Forgetting in Recurrent Neural Networks Using a Fixed Expansion Layer (Coop, Robert and Arel, Itamar)", "Compete to Compute (Srivastava, Rupesh Kumar and Masci, Jonathan and Kazerounian, Sohrob and Gomez, Faustino and Schmidhuber, J\u00fcrgen)", "Mitigation of Catastrophic Interference in Neural Networks Using a Fixed Expansion Layer (Coop, Robert and Arel, Itamar)", "Synaptic Plasticity: Taming the Beast (Abbott, L F and Nelson, Sacha B)", "Architecture Matters in Continual Learning (Mirzadeh, Seyed Iman and Chaudhry, Arslan and Yin, Dong and Nguyen, Timothy and Pascanu, Razvan and Gorur, Dilan and Farajtabar, Mehrdad)", "Continual Learning in the Teacher-Student Setup: Impact of Task Similarity (Lee, Sebastian and Goldt, Sebastian and Saxe, Andrew)", "Continual Learning in Deep Networks: An Analysis of the Last Layer (Lesort, Timoth\u00e9e and George, Thomas and Rish, Irina)", "Understanding Continual Learning Settings with Data Distribution Drift Analysis (Lesort, Timoth\u00e9e and Caccia, Massimo and Rish, Irina)", "Wide Neural Networks Forget Less Catastrophically (Mirzadeh, Seyed Iman and Chaudhry, Arslan and Hu, Huiyi and Pascanu, Razvan and Gorur, Dilan and Farajtabar, Mehrdad)", "Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics (Ramasesh, Vinay Venkatesh and Dyer, Ethan and Raghu, Maithra)", "Does Continual Learning = Catastrophic Forgetting? (Thai, Anh and Stojanov, Stefan and Rehg, Isaac and Rehg, James M.)", "Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget (Davidson, Guy and Mozer, Michael C)", "Understanding the Role of Training Regimes in Continual Learning (Mirzadeh, Seyed Iman and Farajtabar, Mehrdad and Pascanu, Razvan and Ghasemzadeh, Hassan)", "Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization (Nguyen, Giang and Chen, Shuan and Do, Thao and Jun, Tae Joon and Choi, Ho-Jin and Kim, Daeyoung)", "Toward Understanding Catastrophic Forgetting in Continual Learning (Nguyen, Cuong V and Achille, Alessandro and Lam, Michael and Hassner, Tal and Mahadevan, Vijay and Soatto, Stefano)", "A Study on Catastrophic Forgetting in Deep LSTM Networks (Schak, Monika and Gepperth, Alexander)", "An Empirical Study of Example Forgetting during Deep Neural Network Learning (Toneva, Mariya and Sordoni, Alessandro and des Combes, Remi Tachet and Trischler, Adam and Bengio, Yoshua and Gordon, Geoffrey J)", "Localizing Catastrophic Forgetting in Neural Networks (Wiewel, Felix and Yang, Bin)", "An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks (Goodfellow, Ian J. and Mirza, Mehdi and Xiao, Da and Courville, Aaron and Bengio, Yoshua)", "How Does a Brain Build a Cognitive Code? (Grossberg, Stephen)", "The Organization of Behavior: A Neuropsychological Theory (Hebb, D O)", "Pseudo-Recurrent Connectionist Networks: An Approach to the 'Sensitivity-Stability' Dilemma (French, Robert)", "CHILD: A First Step Towards Continual Learning (Ring, Mark B)", "Learning in the Presence of Concept Drift and Hidden Contexts (Widmer, Gerhard and Kubat, Miroslav)", "Using Semi-Distributed Representations to Overcome Catastrophic Forgetting in Connectionist Networks (French, Robert)", "Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions (Ratcliff, R.)", "The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network (Carpenter, Gail A. and Grossberg, Stephen)", "How Does a Brain Build a Cognitive Code? (Grossberg, Stephen)", "Few-Shot Continual Learning: A Brain-Inspired Approach (Wang, Liyuan and Li, Qian and Zhong, Yi and Zhu, Jun)", "Defining Benchmarks for Continual Few-Shot Learning (Antoniou, Antreas and Patacchiola, Massimiliano and Ochal, Mateusz and Storkey, Amos)", "Tell Me What This Is: Few-Shot Incremental Object Learning by a Robot (Ayub, Ali and Wagner, Alan R.)", "La-MAML: Look-ahead Meta Learning for Continual Learning (Gupta, Gunshi and Yadav, Karmesh and Paull, Liam)", "iTAML: An Incremental Task-Agnostic Meta-learning Approach (Rajasegaran, Jathushan and Khan, Salman and Hayat, Munawar and Khan, Fahad Shahbaz and Shah, Mubarak)", "Wandering within a World: Online Contextualized Few-Shot Learning (Ren, Mengye and Iuzzolino, Michael L and Mozer, Michael C and Zemel, Richard S)", "Few-Shot Class-Incremental Learning (Tao, X. and X., Hong and Chang, X. and Dong, S. and Wei, X. and Gong, Y.)", "Few-Shot Class-Incremental Learning via Feature Space Composition (Zhao, H. and Fu, Y. and Li, X. and Li, S. and Omar, B. and Li, X.)", "Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning (Caccia, Massimo and Rodriguez, Pau and Ostapenko, Oleksiy and Normandin, Fabrice and Lin, Min and Caccia, Lucas and Laradji, Issam and Rish, Irina and Lacoste, Alexande and Vazquez, David and Charlin, Laurent)", "Continuous Meta-Learning without Tasks (Harrison, James and Sharma, Apoorva and Finn, Chelsea and Pavone, Marco)", "Task Agnostic Continual Learning via Meta Learning (He, Xu and Sygnowski, Jakub and Galashov, Alexandre and Rusu, Andrei A and Teh, Yee Whye and Pascanu, Razvan)", "Reconciling Meta-Learning and Continual Learning with Online Mixtures of Tasks (Jerfel, Ghassen and Grant, Erin and Griffiths, Tom and Heller, Katherine A)", "Lifetime Policy Reuse and the Importance of Task Capacity (Bossens, David M. and Sobey, Adam J.)", "Unsupervised Lifelong Learning with Curricula (He, Yi and Chen, Sheng and Wu, Baijun and Yuan, Xu and Wu, Xindong)", "Continuous Coordination As a Realistic Scenario for Lifelong Learning (Nekoei, Hadi and Badrinaaraayanan, Akilesh and Courville, Aaron and Chandar, Sarath)", "Reducing Catastrophic Forgetting When Evolving Neural Networks (Early, Joseph)", "A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning (Garcia, Francisco M and Thomas, Philip S)", "Policy Consolidation for Continual Reinforcement Learning (Kaplanis, Christos and Shanahan, Murray and Clopath, Claudia)", "Continual Learning Exploiting Structure of Fractal Reservoir Computing (Kobayashi, Taisuke and Sugino, Toshiki)", "Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL (Nagabandi, Anusha and Finn, Chelsea and Levine, Sergey)", "Leaky Tiling Activations: A Simple Approach to Learning Sparse Representations Online (Pan, Yangchen and Banman, Kirby and White, Martha)", "Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference (Riemer, Matthew and Cases, Ignacio and Ajemian, Robert and Liu, Miao and Rish, Irina and Tu, Yuhai and Tesauro, Gerald)", "Experience Replay for Continual Learning (Rolnick, David and Ahuja, Arun and Schwarz, Jonathan and Lillicrap, Timothy P and Wayne, Greg)", "Selective Experience Replay for Lifelong Learning (Isele, David and Cosgun, Akansel)", "Continual Reinforcement Learning with Complex Synapses (Kaplanis, Christos and Shanahan, Murray and Clopath, Claudia)", "Unicorn: Continual Learning with a Universal, Off-policy Agent (Mankowitz, Daniel J and \u017d\u00eddek, Augustin and Barreto, Andr\u00e9 and Horgan, Dan and Hessel, Matteo and Quan, John and Oh, Junhyuk and van Hasselt, Hado and Silver, David and Schaul, Tom)", "Lifelong Inverse Reinforcement Learning (Mendez, Jorge A and Shivkumar, Shashank and Eaton, Eric)", "Progress & Compress: A Scalable Framework for Continual Learning (Schwarz, Jonathan and Czarnecki, Wojciech and Luketina, Jelena and Grabska-Barwinska, Agnieszka and Teh, Yee Whye and Pascanu, Razvan and Hadsell, Raia)", "Overcoming Catastrophic Forgetting in Neural Networks (Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia)", "Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory (L\u00fcders, Benno and Schl\u00e4ger, Mikkel and Korach, Aleksandra and Risi, Sebastian)", "Stable Predictive Representations with General Value Functions for Continual Learning (Schlegel, Matthew and White, Adam and White, Martha)", "Continual Learning through Evolvable Neural Turing Machines (Luders, Benno and Schlager, Mikkel and Risi, Sebastian)", "Progressive Neural Networks (Rusu, Andrei A and Rabinowitz, Neil C and Desjardins, Guillaume and Soyer, Hubert and Kirkpatrick, James and Kavukcuoglu, Koray and Pascanu, Razvan and Hadsell, Raia)", "Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets. (Shu, Lei and Liu, Bing and Xu, Hu and Kim, Annice)", "CHILD: A First Step Towards Continual Learning (Ring, Mark B)", "Continual Sequence Generation with Adaptive Compositional Modules (Zhang, Yanzhe and Wang, Xuezhi and Yang, Diyi)", "Continual Learning for Recurrent Neural Networks: An Empirical Evaluation (Cossu, Andrea and Carta, Antonio and Lomonaco, Vincenzo and Bacciu, Davide)", "Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning (Ororbia, Alexander G.)", "Continual Learning with Gated Incremental Memories for Sequential Data Processing (Cossu, Andrea and Carta, Antonio and Bacciu, Davide)", "Organizing Recurrent Network Dynamics by Task-Computation to Enable Continual Learning (Duncker, Lea and Driscoll, Laura N and Shenoy, Krishna V and Sahani, Maneesh and Sussillo, David)", "Meta-Consolidation for Continual Learning (Joseph, K J and Balasubramanian, Vineeth N)", "Compositional Language Continual Learning (Li, Yuanpeng and Zhao, Liang and Church, Kenneth and Elhoseiny, Mohamed)", "Online Continual Learning on Sequences (Parisi, German I and Lomonaco, Vincenzo)", "Unsupervised Progressive Learning and the STAM Architecture (Smith, James and Baer, Seth and Taylor, Cameron and Dovrolis, Constantine)", "Toward Training Recurrent Neural Networks for Lifelong Learning (Sodhani, Shagun and Chandar, Sarath and Bengio, Yoshua)", "Semi-Supervised Tuning from Temporal Coherence (Maltoni, Davide and Lomonaco, Vincenzo)", "Self-Refreshing Memory in Artificial Neural Networks: Learning Temporal Sequences without Catastrophic Forgetting (Ans, Bernard and Rousset, St\u00e9phane and French, Robert M. and Musca, Serban)", "Using Pseudo-Recurrent Connectionist Networks to Solve the Problem of Sequential Learning (French, Robert)", "Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes (Lesort, Timoth'ee)", "Continual Learning in Neural Networks (Aljundi, Rahaf)", "Continual Deep Learning via Progressive Learning (Fayek, Haytham M.)", "Continual Learning with Deep Architectures (Lomonaco, Vincenzo)", "Explanation-Based Neural Network Learning: A Lifelong Learning Approach (Thrun, Sebastian)", "Continual Learning in Reinforcement Environments (Ring, Mark)", "Foundational Models for Continual Learning: An Empirical Study of Latent Replay (Ostapenko, Oleksiy and Lesort, Timothee and Rodr\u00edguez, Pau and Arefin, Md Rifat and Douillard, Arthur and Rish, Irina and Charlin, Laurent)", "Brain-Inspired Replay for Continual Learning with Artificial Neural Networks (van de Ven, Gido M. and Siegelmann, Hava T. and Tolias, Andreas S.)", "Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (Rostami, Mohammad and Kolouri, Soheil and Pilly, Praveen K)", "Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (Rostami, Mohammad and Kolouri, Soheil and Pilly, Praveen K.)", "Continual Learning of New Sound Classes Using Generative Replay (Wang, Zhepei and Subakan, Cem and Tzinis, Efthymios and Smaragdis, Paris and Charlin, Laurent)", "Generative Replay with Feedback Connections as a General Strategy for Continual Learning (van de Ven, Gido M. and Tolias, Andreas S.)", "Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory (L\u00fcders, Benno and Schl\u00e4ger, Mikkel and Korach, Aleksandra and Risi, Sebastian)", "Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches (Lomonaco, Vincenzo and Maltoni, Davide and Pellegrini, Lorenzo)", "Linear Mode Connectivity in Multitask and Continual Learning (Mirzadeh, Seyed Iman and Farajtabar, Mehrdad and Gorur, Dilan and Pascanu, Razvan and Ghasemzadeh, Hassan)", "Efficient Continual Learning in Neural Networks with Embedding Regularization (Pomponi, Jary and Scardapane, Simone and Lomonaco, Vincenzo and Uncini, Aurelio)", "Efficient Lifelong Learning with A-GEM (Chaudhry, Arslan and Ranzato, Marc'Aurelio and Rohrbach, Marcus and Elhoseiny, Mohamed)", "Single-Net Continual Learning with Progressive Segmented Training (PST) (Du, Xiaocong and Charan, Gouranga and Liu, Frank and Cao, Yu)", "Continuous Learning in Single-Incremental-Task Scenarios (Maltoni, Davide and Lomonaco, Vincenzo)", "Toward Training Recurrent Neural Networks for Lifelong Learning (Sodhani, Shagun and Chandar, Sarath and Bengio, Yoshua)", "Continual Learning of New Sound Classes Using Generative Replay (Wang, Zhepei and Subakan, Cem and Tzinis, Efthymios and Smaragdis, Paris and Charlin, Laurent)", "Lifelong Learning via Progressive Distillation and Retrospection (Hou, Saihui and Pan, Xinyu and Loy, Chen Change and Wang, Zilei and Lin, Dahua)", "Progress & Compress: A Scalable Framework for Continual Learning (Schwarz, Jonathan and Czarnecki, Wojciech and Luketina, Jelena and Grabska-Barwinska, Agnieszka and Teh, Yee Whye and Pascanu, Razvan and Hadsell, Raia)", "Gradient Episodic Memory for Continual Learning (Lopez-Paz, David and Ranzato, Marc'Aurelio)", "Learning to Continually Learn (Beaulieu, Shawn and Frati, Lapo and Miconi, Thomas and Lehman, Joel and Stanley, Kenneth O. and Clune, Jeff and Cheney, Nick)", "Continual Learning with Deep Artificial Neurons (Camp, Blake and Mandivarapu, Jaya Krishna and Estrada, Rolando)", "Meta-Consolidation for Continual Learning (Joseph, K J and Balasubramanian, Vineeth N)", "Meta Continual Learning via Dynamic Programming (Krishnan, R and Balaprakash, Prasanna)", "Online Meta-Learning (Finn, Chelsea and Rajeswaran, Aravind and Kakade, Sham and Levine, Sergey)", "Task Agnostic Continual Learning via Meta Learning (He, Xu and Sygnowski, Jakub and Galashov, Alexandre and Rusu, Andrei A and Teh, Yee Whye and Pascanu, Razvan)", "Meta-Learning Representations for Continual Learning (Javed, Khurram and White, Martha)", "Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference (Riemer, Matthew and Cases, Ignacio and Ajemian, Robert and Liu, Miao and Rish, Irina and Tu, Yuhai and Tesauro, Gerald)", "Meta Continual Learning (Vuorio, Risto and Cho, Dong-Yeon and Kim, Daejoong and Kim, Jiwon)", "Continual Learning in Deep Networks: An Analysis of the Last Layer (Lesort, Timoth\u00e9e and George, Thomas and Rish, Irina)", "Avalanche: An End-to-End Library for Continual Learning (Lomonaco, Vincenzo and Pellegrini, Lorenzo and Cossu, Andrea and Carta, Antonio and Graffieti, Gabriele and Hayes, Tyler L. and De Lange, Matthias and Masana, Marc and Pomponi, Jary and van de Ven, Gido and Mundt, Martin and She, Qi and Cooper, Keiland and Forest, Jeremy and Belouadah, Eden and Calderara, Simone and Parisi, German I. and Cuzzolin, Fabio and Tolias, Andreas and Scardapane, Simone and Antiga, Luca and Amhad, Subutai and Popescu, Adrian and Kanan, Christopher and van de Weijer, Joost and Tuytelaars, Tinne and Bacciu, Davide and Maltoni, Davide)", "CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability (Mundt, Martin and Lang, Steven and Delfosse, Quentin and Kersting, Kristian)", "Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning (Caccia, Massimo and Rodriguez, Pau and Ostapenko, Oleksiy and Normandin, Fabrice and Lin, Min and Caccia, Lucas and Laradji, Issam and Rish, Irina and Lacoste, Alexande and Vazquez, David and Charlin, Laurent)", "Optimal Continual Learning Has Perfect Memory and Is NP-HARD (Knoblauch, Jeremias and Husain, Hisham and Diethe, Tom)", "Regularization Shortcomings for Continual Learning (Lesort, Timoth\u00e9e and Stoian, Andrei and Filliat, David)", "Strategies for Improving Single-Head Continual Learning Performance (El Khatib, Alaa and Karray, Fakhri)", "Towards Robust Evaluations of Continual Learning (Farquhar, Sebastian and Gal, Yarin)", "Don't Forget, There Is More than Forgetting: New Metrics for Continual Learning (D\u00edaz-Rodr\\\u01f5uez, Natalia and Lomonaco, Vincenzo and Filliat, David and Maltoni, Davide)", "Three Scenarios for Continual Learning (van de Ven, Gido M and Tolias, Andreas S)", "Biological Underpinnings for Lifelong Learning Machines (Kudithipudi, Dhireesha and Aguilar-Simon, Mario and Babb, Jonathan and Bazhenov, Maxim and Blackiston, Douglas and Bongard, Josh and Brna, Andrew P. and Chakravarthi Raja, Suraj and Cheney, Nick and Clune, Jeff and Daram, Anurag and Fusi, Stefano and Helfer, Peter and Kay, Leslie and Ketz, Nicholas and Kira, Zsolt and Kolouri, Soheil and Krichmar, Jeffrey L. and Kriegman, Sam and Levin, Michael and Madireddy, Sandeep and Manicka, Santosh and Marjaninejad, Ali and McNaughton, Bruce and Miikkulainen, Risto and Navratilova, Zaneta and Pandit, Tej and Parker, Alice and Pilly, Praveen K. and Risi, Sebastian and Sejnowski, Terrence J. and Soltoggio, Andrea and Soures, Nicholas and Tolias, Andreas S. and Urbina-Mel\u00e9ndez, Dar\u00edo and Valero-Cuevas, Francisco J. and van de Ven, Gido M. and Vogelstein, Joshua T. and Wang, Felix and Weiss, Ron and Yanguas-Gil, Angel and Zou, Xinyun and Siegelmann, Hava)", "Neural Inhibition for Continual Learning and Memory (Barron, Helen C)", "Can Sleep Protect Memories from Catastrophic Forgetting? (Gonzalez, Oscar C and Sokolov, Yury and Krishnan, Giri and Bazhenov, Maxim)", "Synaptic Consolidation: An Approach to Long-Term Learning (Clopath, Claudia)", "The Organization of Behavior: A Neuropsychological Theory (Hebb, D O)", "Negative Transfer Errors in Sequential Cognitive Skills: Strong-but-wrong Sequence Application. (Woltz, Dan J. and Gardner, Michael K. and Bell, Brian G.)", "Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions. (Ratcliff, R)", "Continual Novelty Detection (Aljundi, Rahaf and Reino, Daniel Olmeda and Chumerin, Nikolay and Turner, Richard E.)", "Co\\$2\u0302\\$L: Contrastive Continual Learning (Cha, Hyuntak and Lee, Jaeho and Shin, Jinwoo)", "Sustainable Artificial Intelligence through Continual Learning (Cossu, Andrea and Ziosi, Marta and Lomonaco, Vincenzo)", "Continual Backprop: Stochastic Gradient Descent with Persistent Randomness (Dohare, Shibhansh and Mahmood, A. Rupam and Sutton, Richard S.)", "Continuum: Simple Management of Complex Continual Learning Scenarios (Douillard, Arthur and Lesort, Timoth\u00e9e)", "Posterior Meta-Replay for Continual Learning (Henning, Christian and Cervera, Maria and D'Angelo, Francesco and Oswald, Johannes Von and Traber, Regina and Ehret, Benjamin and Kobayashi, Seijin and Grewe, Benjamin F. and Sacramento, Joao)", "Rethinking the Representational Continuity: Towards Unsupervised Continual Learning (Madaan, Divyam and Yoon, Jaehong and Li, Yuanchun and Liu, Yunxin and Hwang, Sung Ju)", "Representation Memorization for Fast Learning New Knowledge without Forgetting (Mi, Fei and Lin, Tao and Faltings, Boi)", "Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning (Chen, Tianlong and Zhang, Zhenyu and Liu, Sijia and Chang, Shiyu and Wang, Zhangyang)", "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (Hayes, Tyler L and Kanan, Christopher)", "Continual Learning with Bayesian Neural Networks for Non-Stationary Data (Kurle, Richard and Cseke, Botond and Klushyn, Alexej and van der Smagt, Patrick and G\u00fcnnemann, Stephan)", "Energy-Based Models for Continual Learning (Li, Shuang and Du, Yilun and van de Ven, Gido M. and Torralba, Antonio and Mordatch, Igor)", "Continual Learning Using Task Conditional Neural Networks (Li, Honglin and Barnaghi, Payam and Enshaeifar, Shirin and Ganz, Frieder)", "Mnemonics Training: Multi-Class Incremental Learning without Forgetting (Liu, Yaoyao and Liu, An-An and Su, Yuting and Schiele, Bernt and Sun, Qianru)", "Continual Universal Object Detection (Liu, Xialei and Yang, Hao and Ravichandran, Avinash and Bhotika, Rahul and Soatto, Stefano)", "Gradient Projection Memory for Continual Learning (Saha, Gobinda and Roy, Kaushik)", "Structured Compression and Sharing of Representational Space for Continual Learning (Saha, Gobinda and Garg, Isha and Ankit, Aayush and Roy, Kaushik)", "Gated Linear Networks (Veness, Joel and Lattimore, Tor and Budden, David and Bhoopchand, Avishkar and Mattern, Christopher and Grabska-Barwinska, Agnieszka and Sezener, Eren and Wang, Jianan and Toth, Peter and Schmitt, Simon and Hutter, Marcus)", "Lifelong Graph Learning (Wang, Chen and Qiu, Yuheng and Scherer, Sebastian)", "Superposition of Many Models into One (Cheung, Brian and Terekhov, Alex and Chen, Yubei and Agrawal, Pulkit and Olshausen, Bruno)", "Continual Learning in Practice (Diethe, Tom and Borchert, Tom and Thereska, Eno and Balle, Borja and Lawrence, Neil)", "Dynamically Constraining Connectionist Networks to Produce Distributed, Orthogonal Representations to Reduce Catastrophic Interference (French, Robert)", "Continual Learning via Neural Pruning (Golkar, Siavash and Kagan, Michael and Cho, Kyunghyun)", "BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach (Kuzina, Anna and Egorov, Evgenii and Burnaev, Evgeny)", "Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild (Lee, Kibok and Lee, Kimin and Shin, Jinwoo and Lee, Honglak)", "Continual Learning Using Bayesian Neural Networks (Li, HongLin and Barnaghi, Payam and Enshaeifar, Shirin and Ganz, Frieder)", "Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition (Mundt, Martin and Majumder, Sagnik and Pliushch, Iuliia and Hong, Yong Won and Ramesh, Visvanathan)", "Continual Rare-Class Recognition with Emerging Novel Subclasses (Nguyen, Hung and Wang, Xuejian and Akoglu, Leman)", "Random Path Selection for Incremental Learning (Rajasegaran, Jathushan and Hayat, Munawar and Fahad, Salman Khan and Khan, Shahbaz and Shao, Ling)", "Improving and Understanding Variational Continual Learning (Swaroop, Siddharth and Nguyen, Cuong V and Bui, Thang D and Turner, Richard E)", "Continual Learning via Online Leverage Score Sampling (Teng, Dan and Dasgupta, Sakyasingha)", "Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class Classifiers (Ye, Xin and Zhu, Qiuyu)", "Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies (Achille, Alessandro and Eccles, Tom and Matthey, Loic and Burgess, Christopher P. and Watters, Nick and Lerchner, Alexander and Higgins, Irina)", "Overcoming Catastrophic Interference Using Conceptor-Aided Backpropagation (He, Xu and Jaeger, Herbert)", "Less-Forgetful Learning for Domain Expansion in Deep Neural Networks (Jung, Heechul and Ju, Jeongwoo and Jung, Minju and Kim, Junmo)", "Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (Mallya, Arun and Davis, Dillon and Lazebnik, Svetlana)", "Adding New Tasks to a Single Network with Weight Transformations Using Binary Masks (Mancini, Massimiliano and Ricci, Elisa and Caputo, Barbara and Bul\u00f2, Samuel Rota)", "Variational Continual Learning (Nguyen, Cuong V and Li, Yingzhen and Bui, Thang D and Turner, Richard E)", "Task Agnostic Continual Learning Using Online Variational Bayes (Zeno, Chen and Golan, Itay and Hoffer, Elad and Soudry, Daniel)", "Encoder Based Lifelong Learning (Triki, Amal Rannen and Aljundi, Rahaf and Blaschko, Mathew B. and Tuytelaars, Tinne)", "Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios (K\u00e4ding, Christoph and Rodner, Erik and Freytag, Alexander and Denzler, Joachim)", "Using Hindsight to Anchor Past Knowledge in Continual Learning (Chaudhry, Arslan and Gordo, Albert and Dokania, Puneet K. and Torr, Philip and Lopez-Paz, David)", "Contrastive Continual Learning with Feature Propagation (Han, Xuejun and Guo, Yuhong)", "Gradient Projection Memory for Continual Learning (Saha, Gobinda and Garg, Isha and Roy, Kaushik)", "Gradient Projection Memory for Continual Learning (Saha, Gobinda and Garg, Isha and Roy, Kaushik)", "Modeling the Background for Incremental Learning in Semantic Segmentation (Cermelli, Fabio and Mancini, Massimiliano and Bul\u00f2, Samuel Rota and Ricci, Elisa and Caputo, Barbara)", "PLOP: Learning without Forgetting for Continual Semantic Segmentation (Douillard, Arthur and Chen, Yifu and Dapogny, Arnaud and Cord, Matthieu)", "Insights from the Future for Continual Learning (Douillard, Arthur and Valle, Eduardo and Ollion, Charles and Robert, Thomas and Cord, Matthieu)", "PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (Douillard, Arthur and Cord, Matthieu and Ollion, Charles and Robert, Thomas and Valle, Eduardo)", "Uncertainty-Guided Continual Learning with Bayesian Neural Networks (Ebrahimi, Sayna and Elhoseiny, Mohamed and Darrell, Trevor and Rohrbach, Marcus)", "Continual Learning of Object Instances (Parshotam, Kishan and Kilickaya, Mert)", "Efficient Continual Learning in Neural Networks with Embedding Regularization (Pomponi, Jary and Scardapane, Simone and Lomonaco, Vincenzo and Uncini, Aurelio)", "Continual Learning with Hypernetworks (von Oswald, Johannes and Henning, Christian and Sacramento, Jo\u00e3o and Grewe, Benjamin F)", "Uncertainty-Based Continual Learning with Adaptive Regularization (Ahn, Hongjoon and Cha, Sungmin and Lee, Donggyu and Moon, Taesup)", "Learning without Memorizing (Dhar, Prithviraj and Vikram Singh, Rajat and Peng, Kuan-Chuan and Wu, Ziyan and Chellappa, Rama)", "Incremental Learning Techniques for Semantic Segmentation (Michieli, Umberto and Zanuttigh, Pietro)", "Functional Regularisation for Continual Learning Using Gaussian Processes (Titsias, Michalis K and Schwarz, Jonathan and Matthews, Alexander G de G and Pascanu, Razvan and Teh, Yee Whye)", "Rotate Your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (Liu, Xialei and Masana, Marc and Herranz, Luis and Van de Weijer, Joost and Lopez, Antonio M. and Bagdanov, Andrew D)", "Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting (Ritter, Hippolyt and Botev, Aleksandar and Barber, David)", "Overcoming Catastrophic Forgetting with Hard Attention to the Task (Serr\u00e0, Joan and Sur\u00eds, D\\d\u0301ac and Miron, Marius and Karatzoglou, Alexandros)", "Overcoming Catastrophic Forgetting in Neural Networks (Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia)", "Overcoming Catastrophic Forgetting by Incremental Moment Matching (Lee, Sang-Woo and Kim, Jin-Hwa and Jun, Jaehyun and Ha, Jung-Woo and Zhang, Byoung-Tak)", "Lifelong Generative Modeling (Ramapuram, Jason and Gregorova, Magda and Kalousis, Alexandros)", "Continual Learning in Generative Adversarial Nets (Seff, Ari and Beatson, Alex and Suo, Daniel and Liu, Han)", "Incremental Learning of Object Detectors without Catastrophic Forgetting (Shmelkov, Konstantin and Schmid, Cordelia and Alahari, Karteek)", "Continual Learning Through Synaptic Intelligence (Zenke, Friedemann and Poole, Ben and Ganguli, Surya)", "Learning without Forgetting (Li, Zhizhong and Hoiem, Derek)", "It's All About Consistency: A Study on Memory Composition for Replay-Based Methods in Continual Learning (Hurtado, Julio and Raymond-Saez, Alain and Araujo, Vladimir and Lomonaco, Vincenzo and Bacciu, Davide)", "Foundational Models for Continual Learning: An Empirical Study of Latent Replay (Ostapenko, Oleksiy and Lesort, Timothee and Rodr\u00edguez, Pau and Arefin, Md Rifat and Douillard, Arthur and Rish, Irina and Charlin, Laurent)", "Using Hindsight to Anchor Past Knowledge in Continual Learning (Chaudhry, Arslan and Gordo, Albert and Dokania, Puneet K. and Torr, Philip and Lopez-Paz, David)", "Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams (De Lange, Matthias and Tuytelaars, Tinne)", "Replay in Deep Learning: Current Approaches and Missing Biological Elements (Hayes, Tyler L. and Krishnan, Giri P. and Bazhenov, Maxim and Siegelmann, Hava T. and Sejnowski, Terrence J. and Kanan, Christopher)", "Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay -- 3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A Continual Object Classification (Kurniawan, Muhammad Rifki and Wei, Xing and Gong, Yihong)", "Distilled Replay: Overcoming Forgetting through Synthetic Samples (Rosasco, Andrea and Carta, Antonio and Cossu, Andrea and Lomonaco, Vincenzo and Bacciu, Davide)", "Online Coreset Selection for Rehearsal-based Continual Learning (Yoon, Jaehong and Madaan, Divyam and Yang, Eunho and Hwang, Sung Ju)", "CALM: Continuous Adaptive Learning for Language Modeling (Arumae, Kristjan and Bhatia, Parminder)", "REMIND Your Neural Network to Prevent Catastrophic Forgetting (Hayes, Tyler L. and Kafle, Kushal and Shrestha, Robik and Acharya, Manoj and Kanan, Christopher)", "CLOPS: Continual Learning of Physiological Signals (Kiyasseh, Dani and Zhu, Tingting and Clifton, David A)", "Continual Learning with Bayesian Neural Networks for Non-Stationary Data (Kurle, Richard and Cseke, Botond and Klushyn, Alexej and van der Smagt, Patrick and G\u00fcnnemann, Stephan)", "GDumb: A Simple Approach That Questions Our Progress in Continual Learning (Prabhu, Ameya and Torr, Philip H. 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1.2530245780944824, 0.6625623106956482, 1.0330126285552979, 1.8063420057296753, 1.3947556018829346, 1.4308031797409058, -0.09738273918628693, 0.5324493050575256, 1.017632007598877, 1.3187015056610107, 0.9822409152984619, 1.8868927955627441, -0.4214904010295868, -0.2858911156654358, -0.05774369090795517, -0.154948890209198, 0.006087685469537973, -0.19439049065113068]} \ No newline at end of file diff --git a/papers.md b/papers.md index ba91b5a..c3266a4 100644 --- a/papers.md +++ b/papers.md @@ -13,7 +13,7 @@ In this section we maintain an updated list of publications related to Continual This references list is automatically generated by a single bibtex file maintained by the ContinualAI community through an open Mendeley group! Join our group [here](https://www.mendeley.com/community/continual-learning-papers/?__cf_chl_captcha_tk__=d4a16b2e7ba082bc24fbb7fb7cbba3149969ff33-1589287156-0-Aa1Wr5LQkCQwqaFz3Ho_5lc1NnR1Dn6bDEe8fZlbjwIKIQy-b28wKYYcbcdksrP0zP2e8x1BfyD3V0eiZWMVdFQ0AqGzm8qHQYklAGUPz0COhkQec_hu0O1_XFh7PtHXNKfIiyBb9TppP05KlSNIIxJk2u7lNAlGw1pWscPNhIvk_4p-5XDf-YFu3HpCDYN1IQ7bQgkGqMRYAdYtZS7gq1C_w6iykd2sA6IawsIbaCtdW08H77e-7T7rEdo91HndXMIJgV5UQBnJSwRHOl-g-8EKrUWUDHBdGQgLhiJli4y16AAGu979jkOyhtS7onFfRNXdUELb3pOiD0YS5zCnmHM6PURblRyb6HA2ma7f0JIC8DIjmK2xCcRlYqgiNrWVS3oEbS6uqn63IdxYgoSLq6vo68mS1e_Or8LGRpOE8uemjJfbVnPR4RI3mqevN5OxbgWz-CYkElgLAXeaEFqVitVCsaEmDygdit6flohhCpCd5vVs6gv1t_ALu6Q7nZIbFc386zRcqDb-MhIV7BpRIOA) to add a reference to your paper! Please, remember to follow the (very simple) [contributions guidelines](https://github.com/ContinualAI/wiki#how-to-contribute-to-the-continualai-database-of-publications) when adding new papers. -**Search among 332 papers!** +**Search among 338 papers!** - - - + + + -

-

-

- + - [CLOPS: Continual Learning of Physiological Signals](http://arxiv.org/abs/2004.09578) by Dani Kiyasseh, Tingting Zhu and David A Clifton. *arXiv*, 2020. + + + + + + +

+

+

+ - [Structured Ensembles: An Approach to Reduce the Memory Footprint of Ensemble Methods](https://linkinghub.elsevier.com/retrieve/pii/S0893608021003579) by Jary Pomponi, Simone Scardapane and Aurelio Uncini. *Neural Networks*, 407--418, 2021. + + + + + + +

+

+

+ - [Continual Learning with Adaptive Weights (CLAW)](https://openreview.net/forum?id=Hklso24Kwr) by Tameem Adel, Han Zhao and Richard E Turner. *International Conference on Learning Representations*, 2020. [cifar] [mnist] [omniglot] - - - + + + -

-

-

- + - [Stream-51: Streaming Classification and Novelty Detection From Videos](https://openaccess.thecvf.com/content_CVPRW_2020/html/w15/Roady_Stream-51_Streaming_Classification_and_Novelty_Detection_From_Videos_CVPRW_2020_paper.html) by Ryne Roady, Tyler L. Hayes, Hitesh Vaidya and Christopher Kanan. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops*, 228--229, 2020. - - - + + + -

-

-

- + - [Selfless Sequential Learning](https://openreview.net/forum?id=Bkxbrn0cYX) by Rahaf Aljundi, Marcus Rohrbach and Tinne Tuytelaars. *ICLR*, 2019. [cifar] [mnist] [sparsity] - - - + + + -

-

-

- + - [Backpropamine: Training Self-Modifying Neural Networks with Differentiable Neuromodulated Plasticity](https://openreview.net/pdf?id=r1lrAiA5Ym) by Thomas Miconi, Aditya Rawal, Jeff Clune and Kenneth O Stanley. *ICLR*, 2019. - - - + + + -

-

-

- + - [Differentiable Plasticity: Training Plastic Neural Networks with Backpropagation](http://proceedings.mlr.press/v80/miconi18a.html) by Thomas Miconi, Kenneth Stanley and Jeff Clune. *International Conference on Machine Learning*, 3559--3568, 2018. - - - + + + -

-

-

- + - [Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization](http://arxiv.org/abs/2001.01578) by Giang Nguyen, Shuan Chen, Thao Do, Tae Joon Jun, Ho-Jin Choi and Daeyoung Kim. *arXiv*, 2020. [vision] - - - + + + -

-

-

- + - [CHILD: A First Step Towards Continual Learning](https://doi.org/10.1023/A:1007331723572) by and Mark B Ring. *Machine Learning*, 77--104, 1997. - - - + + + -

-

-

- + - [iTAML: An Incremental Task-Agnostic Meta-learning Approach](https://openaccess.thecvf.com/content_CVPR_2020/html/Rajasegaran_iTAML_An_Incremental_Task-Agnostic_Meta-learning_Approach_CVPR_2020_paper.html) by Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan and Mubarak Shah. *IEEE/CVF Conference on Computer Vision and Pattern Recognition*, 13588---13597, 2020. [cifar] [imagenet] - - - + + + -

-

-

- + - [Online Continual Learning on Sequences](http://arxiv.org/abs/2003.09114) by German I Parisi and Vincenzo Lomonaco. *Studies in Computational Intelligence*, 2020. [framework] - - - + + + -

-

-

- + ### Dissertation and Theses @@ -12172,11 +12312,11 @@ In this section we maintain a list of all the dissertation and thesis produced o - [Large-Scale Deep Class-Incremental Learning. (Apprentissage Incrémental Profond à Large ̧́helle)](https://tel.archives-ouvertes.fr/tel-03478553) by and Eden Belouadah. , 2021. - - - + + + -

-

-

- + - [Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes](http://arxiv.org/abs/2007.00487) by and Timoth'ee Lesort. *arXiv*, 2020. [cifar] [framework] [generative] [mnist] [vision] - - - + + + -

-

-

- + - [Open Set Classification for Deep Learning in Large-Scale and Continual Learning Models](https://scholarworks.rit.edu/theses/10592) by and Ryne Roady. *Theses*, 2020. - - - + + + -

-

-

- + - [Continual Deep Learning via Progressive Learning](http://researchbank.rmit.edu.au/eserv/rmit:162646/Fayek.pdf) by and Haytham M. Fayek. *RMIT University*, 2019. [audio] [cifar] [imagenet] [sparsity] - - - + + + -

-

-

- + - [Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay](http://arxiv.org/abs/1903.04566) by Mohammad Rostami, Soheil Kolouri and Praveen K Pilly. *arXiv*, 2019. - - - + + + -

-

-

- + - [Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory](https://link.springer.com/chapter/10.1007/978-3-319-55849-3_57) by Benno Lüders, Mikkel Schläger, Aleksandra Korach and Sebastian Risi. *Applications of Evolutionary Computation*, 886--901, 2017. - - - + + + -

-

-

- + - [Linear Mode Connectivity in Multitask and Continual Learning](https://arxiv.org/abs/2010.04495) by Seyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Gorur, Razvan Pascanu and Hassan Ghasemzadeh. *arXiv*, 2020. [cifar] [experimental] [mnist] - - - + + + -

-

-

- + - [Efficient Continual Learning in Neural Networks with Embedding Regularization](https://linkinghub.elsevier.com/retrieve/pii/S092523122030151X) by Jary Pomponi, Simone Scardapane, Vincenzo Lomonaco and Aurelio Uncini. *Neurocomputing*, 139--148, 2020. - - - + + + -

-

-

- + - [Efficient Lifelong Learning with A-GEM](http://arxiv.org/abs/1812.00420) by Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach and Mohamed Elhoseiny. *ICLR*, 2019. [cifar] [mnist] - - - + + + -

-

-

- + - [Single-Net Continual Learning with Progressive Segmented Training (PST)](http://arxiv.org/abs/1905.11550) by Xiaocong Du, Gouranga Charan, Frank Liu and Yu Cao. *arXiv*, 1629--1636, 2019. [cifar] - - - + + + -

-

-

- + - [Avalanche: An End-to-End Library for Continual Learning](http://arxiv.org/abs/2104.00405) by Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido van de Ven, Martin Mundt, Qi She, Keiland Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German I. Parisi, Fabio Cuzzolin, Andreas Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu and Davide Maltoni. *CLVision Workshop at CVPR*, 2021. - - - + + + -

-

-

- + - [Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning](http://arxiv.org/abs/2003.05856) by Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam Laradji, Irina Rish, Alexande Lacoste, David Vazquez and Laurent Charlin. *arXiv*, 2020. [fashion] [framework] [mnist] - - - + + + -

-

-

- + - [Strategies for Improving Single-Head Continual Learning Performance](http://link.springer.com/10.1007/978-3-030-27202-9_41) by Alaa El Khatib and Fakhri Karray. *Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*, 452--460, 2019. [cifar] [mnist] - - - + + + -

-

-

- + ### Neuroscience @@ -16122,7 +16262,7 @@ In this section we list all the other papers not appearing in at least one of th

-

-

- + - [Initial Classifier Weights Replay for Memoryless Class Incremental Learning](https://www.bmvc2020-conference.com/assets/papers/0743.pdf) by Eden Belouadah, Adrian Popescu and Ioannis Kanellos. *31st British Machine Vision Conference 2020, BMVC 2020, Virtual Event, UK, September 7-10, 2020*, 2020. - - - + + + -

-

-

- + - [Continual Learning with Bayesian Neural Networks for Non-Stationary Data](https://iclr.cc/virtual_2020/poster_SJlsFpVtDB.html) by Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt and Stephan Günnemann. *Eighth International Conference on Learning Representations*, 2020. [bayes] - - - + + + -

-

-

- -- [Energy-Based Models for Continual Learning](http://arxiv.org/abs/2011.12216) by Shuang Li, Yilun Du, Gido M. van de Ven, Antonio Torralba and Igor Mordatch. *arXiv*, 2020. [cifar] [experimental] [mnist] + +- [Continual Learning Using Task Conditional Neural Networks](http://arxiv.org/abs/2005.05080) by Honglin Li, Payam Barnaghi, Shirin Enshaeifar and Frieder Ganz. *arXiv*, 2020. [cifar] [mnist] - - - + + + -

-

-

- -- [Continual Universal Object Detection](http://arxiv.org/abs/2002.05347) by Xialei Liu, Hao Yang, Avinash Ravichandran, Rahul Bhotika and Stefano Soatto. *arXiv*, 2020. + +- [Mnemonics Training: Multi-Class Incremental Learning without Forgetting](http://arxiv.org/abs/2002.10211) by Yaoyao Liu, An-An Liu, Yuting Su, Bernt Schiele and Qianru Sun. *arXiv*, 2020. [cifar] [imagenet] + + + + + + +

+

+

+ - [Modeling the Background for Incremental Learning in Semantic Segmentation](http://arxiv.org/abs/2002.00718) by Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci and Barbara Caputo. *CVPR*, 9233--9242, 2020. + + + + + + +

+

+

+ - [Foundational Models for Continual Learning: An Empirical Study of Latent Replay](http://arxiv.org/abs/2205.00329) by Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish and Laurent Charlin. *arXiv*, 2022. - - - + + + -

-

-

- + - [REMIND Your Neural Network to Prevent Catastrophic Forgetting](http://arxiv.org/abs/1910.02509) by Tyler L. Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya and Christopher Kanan. *Proceedings of the 2020 ECCV*, 2020. - - - + + + -

-

-

- + - [CLOPS: Continual Learning of Physiological Signals](http://arxiv.org/abs/2004.09578) by Dani Kiyasseh, Tingting Zhu and David A Clifton. *arXiv*, 2020. - - - + + + -

-

-

- + - [Continual Learning with Hypernetworks](https://openreview.net/forum?id=SJgwNerKvB) by Johannes von Oswald, Christian Henning, João Sacramento and Benjamin F Grewe. *International Conference on Learning Representations*, 2020. [cifar] [mnist] - - - + + + -

-

-

- + - [IL2M: Class Incremental Learning With Dual Memory](https://doi.org/10.1109/ICCV.2019.00067) by Eden Belouadah and Adrian Popescu. *2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019*, 583--592, 2019. - - - + + + -

-

-

- + - [Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients](http://arxiv.org/abs/1904.10644) by Yu Chen, Tom Diethe and Neil Lawrence. *arXiv*, 2019. [bayes] - - - + + + -

-

-

- + - [Continual Learning for Recurrent Neural Networks: An Empirical Evaluation](https://www.sciencedirect.com/science/article/pii/S0893608021002847) by Andrea Cossu, Antonio Carta, Vincenzo Lomonaco and Davide Bacciu. *Neural Networks*, 607--627, 2021. [rnn] - - - + + + -

-

-

- + - [Generative Models from the Perspective of Continual Learning](http://arxiv.org/abs/1812.09111) by Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Andrei Stoian and David Filliat. *Proceedings of the International Joint Conference on Neural Networks*, 2018. [cifar] [generative] [mnist]