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In continual learning (CL), the goal is to design models that can learn a sequence of tasks without catastrophic forgetting.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G Stork · 1993
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Lifelong learning algorithms
Sebastian Thrun · 1998
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A model of inductive bias learning
Jonathan Baxter · 2000
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Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael I Jordan · 2002
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A few notes on statistical learning theory
Shahar Mendelson · 2003
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Local rademacher complexities
Peter L Bartlett, Olivier Bousquet, and Shahar Mendelson · 2005
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On the theory of transfer learning: The importance of task diversity
Nilesh Tripuraneni, Michael I Jordan, and Chi Jin · 2006
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Linear algorithms for online multitask classification
Giovanni Cavallanti, Nicolo Cesa-Bianchi, and Claudio Gentile · 2010
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Smoothness, low noise and fast rates
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
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Oracle inequalities and optimal inference under group sparsity
Karim Lounici, Massimiliano Pontil, Sara Van De Geer, and Alexandre B Tsybakov · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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How do humans sketch objects?
Mathias Eitz, James Hays, and Marc Alexa · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Excess risk bounds for multitask learning with trace norm regularization
Massimiliano Pontil and Andreas Maurer · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Large-scale classification of fine-art paintings: Learning the right metric on the right feature
Babak Saleh and Ahmed Elgammal · 2015
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On the uniform convergence of relative frequencies of events to their probabilities
Vladimir N Vapnik and A Ya Chervonenkis · 2015
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Learning the number of neurons in deep networks
Jose M Alvarez and Mathieu Salzmann · 2016
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Eie: Efficient inference engine on compressed deep neural network
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A Horowitz, and William J Dally · 2016
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Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
Earlier work this paper cites.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Distributed multi-task learning with shared representation
Jialei Wang, Mladen Kolar, and Nathan Srebro · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Less is more: Towards compact cnns
Hao Zhou, Jose M Alvarez, and Fatih Porikli · 2016
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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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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Overcoming catastrophic forgetting in neural networks
Generalisation guarantees for continual learning with orthogonal gradient descent
Mehdi Abbana Bennani, Thang Doan, and Masashi Sugiyama · 2020
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Coresets via bilevel optimization for continual learning and streaming
Zalan Borsos, Mojmir Mutny, and Andreas Krause · 2020
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Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
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Functional regularization for representation learning: A unified theoretical perspective
Siddhant Garg and Yingyu Liang · 2020
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A no-free-lunch theorem for multitask learning
Steve Hanneke and Samory Kpotufe · 2020
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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 without forgetting
Zhizhong Li and Derek Hoiem · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Cited alongside, same era.
Efficient processing of deep neural networks: A tutorial and survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S Emer · 2017
Cited alongside, same era.
Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2017
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Continual learning with node-importance based adaptive group sparse regularization
Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon · 2020
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Meta-learning for mixed linear regression
Weihao Kong, Raghav Somani, Zhao Song, Sham Kakade, and Sewoong Oh · 2020
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Proving the lottery ticket hypothesis: Pruning is all you need
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, and Ohad Shamir · 2020
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Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
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Optimal lottery tickets via subsetsum: Logarithmic over-parameterization is sufficient
Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, and Dimitris Papailiopoulos · 2020
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What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
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Extending conditional convolution structures for enhancing multitasking continual learning
Cheng-Hao Tu, Cheng-En Wu, and Chu-Song Chen · 2020
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Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi · 2020
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Understanding and improving information transfer in multi-task learning
Sen Wu, Hongyang R Zhang, and Christopher Ré · 2020
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Dong Yin, Mehrdad Farajtabar, Ang Li, Nir Levine, and Alex Mott · 2020
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Neuron-level structured pruning using polarization regularizer
Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, and Xiang Li · 2020
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Rethinking experience replay: a bag of tricks for continual learning
Pietro Buzzega, Matteo Boschini, Angelo Porrello, and Simone Calderara · 2021
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Provable benefits of overparameterization in model compression: From double descent to pruning neural networks
Xiangyu Chang, Yingcong Li, Samet Oymak, and Christos Thrampoulidis · 2021
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Weighted training for cross-task learning
Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth, and Weijie J Su · 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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A theoretical analysis of catastrophic forgetting through the ntk overlap matrix
Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, and Pierre Alquier · 2021
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Few-shot learning via learning the representation, provably
Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei · 2021
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Sample efficient subspace-based representations for nonlinear meta-learning
Halil Ibrahim Gulluk, Yue Sun, Samet Oymak, and Maryam Fazel · 2021
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Prakhar Kaushik, Alex Gain, Adam Kortylewski, and Alan Yuille · 2021
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Continual learning in the teacher-student setup: Impact of task similarity
Sebastian Lee, Sebastian Goldt, and Andrew Saxe · 2021
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On the power of multitask representation learning in linear mdp
Rui Lu, Gao Huang, and Simon S Du · 2021
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How do quadratic regularizers prevent catastrophic forgetting: The role of interpolation
Ekdeep Singh Lubana, Puja Trivedi, Danai Koutra, and Robert P Dick · 2021
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Generalization guarantees for neural architecture search with train-validation split
Samet Oymak, Mingchen Li, and Mahdi Soltanolkotabi · 2021
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Model zoo: A growing brain that learns continually
Rahul Ramesh and Pratik Chaudhari · 2021
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Towards sample-efficient overparameterized meta-learning
Yue Sun, Adhyyan Narang, Ibrahim Gulluk, Samet Oymak, and Maryam Fazel · 2021
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Non-stationary representation learning in sequential linear bandits
Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, and Fabio Pasqualetti · 2022
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