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Catastrophic forgetting remains a significant challenge to continual learning for decades.
Semi-distributed representations and catastrophic forgetting in connectionist networks
Robert M French · 1992
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Application of artificial neural network for stock market predictions: A review of literature
RK Dase and DD Pawar · 2010
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The MNIST database of handwritten digit images for machine learning research
Li Deng · 2012
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Maxout networks
Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
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Playing Atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, and Ioannis Antonoglou · 2013
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Compete to compute
Rupesh K Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, and Jürgen Schmidhuber · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Comparison of regression and neural network approaches to forecast daily power consumption
Kryukov Dmitri, Agafonova Maria, and Arestova Anna · 2016
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Dynamic network surgery for efficient DNNs
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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PyGame learning environment
Norman Tasfi · 2016
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Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 2017
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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PathNet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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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, et al · 2017
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Learning sparse representations in reinforcement learning with sparse coding
Lei Le, Raksha Kumaraswamy, and Martha White · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan · 2018
Cited alongside, same era.
PackNet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Maxmin Q-learning: Controlling the estimation bias of q-learning
Qingfeng Lan, Yangchen Pan, Alona Fyshe, and Martha White · 2020
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Dynamic sparse training: Find efficient sparse network from scratch with trainable masked layers
Junjie Liu, Zhe Xu, Runbin Shi, Ray C. C. Cheung, and Hayden K.H. So · 2020
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Lifelong policy gradient learning of factored policies for faster training without forgetting
Jorge Mendez, Boyu Wang, and Eric Eaton · 2020
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Dropout as an implicit gating mechanism for continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, and Hassan Ghasemzadeh · 2020
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Go wide, then narrow: Efficient training of deep thin networks
Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le, Qiang Liu, and Dale Schuurmans · 2020
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Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
Cited alongside, same era.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Cited alongside, same era.
Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
Cited alongside, same era.
Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
Cited alongside, same era.
On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
Cited alongside, same era.
Sparse networks from scratch: Faster training without losing performance
Tim Dettmers and Luke Zettlemoyer · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G Bellemare, and David Silver · 2021
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A continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars · 2021
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Clear: An adaptive continual learning framework for regression tasks
Yujiang He and Bernhard Sick · 2021
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Functional regularization for reinforcement learning via learned Fourier features
Alexander Li and Deepak Pathak · 2021
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Ternary feature masks: zero-forgetting for task-incremental learning
Marc Masana, Tinne Tuytelaars, and Joost Van de Weijer · 2021
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Extreme memorization via scale of initialization
Harsh Mehta, Ashok Cutkosky, and Behnam Neyshabur · 2021
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Deep learning-based weather prediction: a survey
Xiaoli Ren, Xiaoyong Li, Kaijun Ren, Junqiang Song, Zichen Xu, Kefeng Deng, and Xiang Wang · 2021
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Algorithmic insights on continual learning from fruit flies
Yang Shen, Sanjoy Dasgupta, and Saket Navlakha · 2021
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SpaceNet: Make free space for continual learning
Ghada Sokar, Decebal Constantin Mocanu, and Mykola Pechenizkiy · 2021
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Sparsity and heterogeneous dropout for continual learning in the null space of neural activations
Ali Abbasi, Parsa Nooralinejad, Vladimir Braverman, Hamed Pirsiavash, and Soheil Kolouri · 2022
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Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2022
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Modular lifelong reinforcement learning via neural composition
Jorge A Mendez, Harm van Seijen, and Eric Eaton · 2022
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Fuzzy tiling activations: A simple approach to learning sparse representations online
Yangchen Pan, Kirby Banman, and Martha White · 2022
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Asher Trockman and J Zico Kolter · 2022
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Cold start streaming learning for deep networks
Cameron R Wolfe and Anastasios Kyrillidis · 2022
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Sparse distributed memory is a continual learner
Trenton Bricken, Xander Davies, Deepak Singh, Dmitry Krotov, and Gabriel Kreiman · 2023
Closest in time.
Memory-efficient reinforcement learning with value-based knowledge consolidation
Qingfeng Lan, Yangchen Pan, Jun Luo, and A Rupam Mahmood · 2023
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Sparse coding in a dual memory system for lifelong learning
Fahad Sarfraz, Elahe Arani, and Bahram Zonooz · 2023
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A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
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