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A growing body of research in continual learning focuses on the catastrophic forgetting problem.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 1902
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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A lifelong learning perspective for mobile robot control
S. Thrun · 1994
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Bagging predictors
Leo Breiman · 1996
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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Ensemble selection from libraries of models
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes · 2004
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Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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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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Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 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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Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger · 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, and et al · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence, 2017
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Essentially no barriers in neural network energy landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred Hamprecht · 2018
Cited alongside, same era.
Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
Cited alongside, same era.
Supermasks in superposition
Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi · 2020
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Dong Yin, Mehrdad Farajtabar, Ang Li, Nir Levine, and Alex Mott · 2020
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Loss surface simplexes for mode connecting volumes and fast ensembling
Gregory W. Benton, Wesley Maddox, Sanae Lotfi, and Andrew Gordon Wilson · 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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Learning a subspace of policies for online adaptation in reinforcement learning
Jean-Baptiste Gaya, Laure Soulier, and Ludovic Denoyer · 2021
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Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Notes on contemporary machine learning for physicists
Jared Kaplan · 2019
Cited alongside, same era.
Habitat: A Platform for Embodied AI Research
Manolis Savva*, Abhishek Kadian*, Oleksandr Maksymets*, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra · 2019
Cited alongside, same era.
Generalisation guarantees for continual learning with orthogonal gradient descent
Mehdi Abbana Bennani, Thang Doan, and Masashi Sugiyama · 2020
Cited alongside, same era.
Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
Cited alongside, same era.
Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M Dai, and Dustin Tran · 2020
Cited alongside, same era.
Continual learning: Tackling catastrophic forgetting in deep neural networks with replay processes
Timothée Lesort · 2020
Cited alongside, same era.
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Task-agnostic continual learning with hybrid probabilistic models
Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, and Razvan Pascanu · 2021
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Continual learning in deep networks: an analysis of the last layer
Timothée Lesort, Thomas George, and Irina Rish · 2021
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Linear mode connectivity in multitask and continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Gorur, Razvan Pascanu, and Hassan Ghasemzadeh · 2021
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Gradient projection memory for continual learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2021
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Habitat 2.0: Training home assistants to rearrange their habitat
Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, and Dhruv Batra · 2021
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Learning neural network subspaces
Mitchell Wortsman, Maxwell Horton, Carlos Guestrin, Ali Farhadi, and Mohammad Rastegari · 2021
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On anytime learning at macroscale
Lucas Caccia, Jing Xu, Myle Ott, Marcaurelio Ranzato, and Ludovic Denoyer · 2022
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Rose: Robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams
Alberto Cano and Bartosz Krawczyk · 2022
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Coscl: Cooperation of small continual learners is stronger than a big one
Liyuan Wang, Xingxing Zhang, Qian Li, Jun Zhu, and Yi Zhong · 2022
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