Fetching the paper…
Reading the bibliography…
Naively trained neural networks tend to experience catastrophic forgetting in sequential task settings, where data from previous tasks are unavailable.
A Generalized Probability Density Function for Double-Bounded Random Processes
Ponnambalam Kumaraswamy · 1980
Earlier work this paper cites.
A Generalized Probability Density Function for Double-Bounded Random Processes
Ponnambalam Kumaraswamy · 1980
Earlier work this paper cites.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions
Roger Ratcliff · 1990
Earlier work this paper cites.
Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions
Roger Ratcliff · 1990
Earlier work this paper cites.
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Ronald J Williams · 1992
Earlier work this paper cites.
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Ronald J Williams · 1992
Earlier work this paper cites.
Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Gradient-based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Gradient-based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Infinite Latent Feature Models and the Indian Buffet Process
Zoubin Ghahramani and Thomas L Griffiths · 2006
Earlier work this paper cites.
Infinite Latent Feature Models and the Indian Buffet Process
Zoubin Ghahramani and Thomas L Griffiths · 2006
Earlier work this paper cites.
Table of integrals, series, and products
I. S. Gradshteyn and I. M. Ryzhik · 2007
Earlier work this paper cites.
Stick-breaking construction for the indian buffet process
Yee Whye Teh, Dilan Grür, and Zoubin Ghahramani · 2007
Earlier work this paper cites.
Table of integrals, series, and products
I. S. Gradshteyn and I. M. Ryzhik · 2007
Earlier work this paper cites.
Stick-breaking construction for the indian buffet process
Yee Whye Teh, Dilan Grür, and Zoubin Ghahramani · 2007
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
An Empirical Investigation of Catastrophic Forgetting in Gradient-based Neural Networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
Earlier work this paper cites.
Auto-encoding variational bayes, 2013
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Handbook of Differential Entropy
Joseph Victor Michalowicz, Jonathan M. Nichols, and Frank Bucholtz · 2013
Earlier work this paper cites.
An Empirical Investigation of Catastrophic Forgetting in Gradient-based Neural Networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
Earlier work this paper cites.
Auto-encoding variational bayes, 2013
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Handbook of Differential Entropy
Joseph Victor Michalowicz, Jonathan M. Nichols, and Frank Bucholtz · 2013
Earlier work this paper cites.
Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Uncertainty in Deep Learning
Yarin Gal · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Stick-breaking variational autoencoders, 2016
Eric Nalisnick and Padhraic Smyth · 2016
Cited alongside, same era.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Cited alongside, same era.
Uncertainty in Deep Learning
Yarin Gal · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Memory Aware Synapses: Learning What (not) to Forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Later among the works it cites.
Towards Robust Evaluations of Continual Learning
Sebastian Farquhar and Yarin Gal · 2018
Later among the works it cites.
Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
Later among the works it cites.
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Later among the works it cites.
Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Stick-breaking variational autoencoders, 2016
Eric Nalisnick and Padhraic Smyth · 2016
Cited alongside, same era.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Cited alongside, same era.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Overcoming Catastrophic Forgetting in Neural Networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
Cited alongside, same era.
Learning Without Forgetting
Zhizhong Li and Derek Hoiem · 2017
Cited alongside, same era.
Gradient Episodic Memory for Continual Learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Cited alongside, same era.
Later among the works it cites.
Variational Continual Learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2018
Later among the works it cites.
Detecting and Classifying Lesions in Mammograms with Deep Learning
Dezső Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner, and István Csabai · 2018
Later among the works it cites.
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
Later among the works it cites.
Progress & Compress: A Scalable Framework for Continual Learning
Jonathan Schwarz, Jelena Luketina, Wojciech M Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Later among the works it cites.
Understanding measures of uncertainty for adversarial example detection
Lewis Smith and Yarin Gal · 2018
Later among the works it cites.
Generative Replay with Feedback Connections as a General Strategy for Continual Learning
Gido M van de Ven and Andreas S Tolias · 2018
Later among the works it cites.
Reinforced Continual Learning
Ju Xu and Zhanxing Zhu · 2018
Later among the works it cites.
Lifelong Learning with Dynamically Dxpandable Networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
Later among the works it cites.
Task-Free Continual Learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
Later among the works it cites.
A Unifying Bayesian View of Continual Learning
Sebastian Farquhar and Yarin Gal · 2019
Later among the works it cites.
Compacting, Picking and Growing for Unforgetting Continual Learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 2019
Later among the works it cites.
Nonparametric Bayesian Structure Adaptation for Continual Learning
Abhishek Kumar, Sunabha Chatterjee, and Piyush Rai · 2019
Later among the works it cites.
Continual Lifelong Learning with Neural Networks: A Review
German Ignacio Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter · 2019
Later among the works it cites.
Experience Replay for Continual Learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Later among the works it cites.
Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2019
Later among the works it cites.
Side-Tuning: Network Adaptation via Additive Side Networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, and Jitendra Malik · 2019
Later among the works it cites.
Task-Free Continual Learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
Later among the works it cites.
A Unifying Bayesian View of Continual Learning
Sebastian Farquhar and Yarin Gal · 2019
Later among the works it cites.
Compacting, Picking and Growing for Unforgetting Continual Learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 2019
Later among the works it cites.
Nonparametric Bayesian Structure Adaptation for Continual Learning
Abhishek Kumar, Sunabha Chatterjee, and Piyush Rai · 2019
Later among the works it cites.
Continual Lifelong Learning with Neural Networks: A Review
German Ignacio Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter · 2019
Later among the works it cites.
Experience Replay for Continual Learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Later among the works it cites.
Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2019
Later among the works it cites.
Side-Tuning: Network Adaptation via Additive Side Networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, and Jitendra Malik · 2019
Later among the works it cites.
Hierarchical indian buffet neural networks for bayesian continual learning
Samuel Kessler, Vu Nguyen, Stefan Zohren, and Stephen Roberts · 2020
Closest in time.
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
Closest in time.
Hierarchical indian buffet neural networks for bayesian continual learning
Samuel Kessler, Vu Nguyen, Stefan Zohren, and Stephen Roberts · 2020
Closest in time.
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
Closest in time.
Efficient continual learning with modular networks and task-driven priors
Tom Veniat, Ludovic Denoyer, and Marc’Aurelio Ranzato · 2021
Closest in time.
Efficient continual learning with modular networks and task-driven priors
Tom Veniat, Ludovic Denoyer, and Marc’Aurelio Ranzato · 2021
Closest in time.