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Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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The malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, T. Hutton, and D. Bignell · 2016
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Learning without Forgetting
Zhizhong Li and Derek Hoiem · 2016
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Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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The Sacred Infrastructure for Computational Research
Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, and Jürgen Schmidhuber · 2017
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CORe50: A New Dataset and Benchmark for Continuous Object Recognition
Vincenzo Lomonaco and Davide Maltoni · 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
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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 Through Synaptic Intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G. Bellemare · 2018
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Lifelong Machine Learning, Second Edition
Zhiyuan Chen and Bing Liu · 2018
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Don’t forget, there is more than forgetting: New metrics for Continual Learning
Natalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat, and Davide Maltoni · 2018
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A Unifying Bayesian View of Continual Learning
Sebastian Farquhar and Yarin Gal · 2018
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Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
Cited alongside, same era.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
Cited alongside, same era.
Artificial intelligence faces reproducibility crisis, 2018
Matthew Hutson · 2018
Cited alongside, same era.
A Correctly Rounded Mixed-Radix Fused-Multiply-Add
C. Jeangoudoux and C. Lauter · 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
Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Embracing Change: Continual Learning in Deep Neural Networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu · 2020
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dtoolai: Reproducibility for deep learning
Matthew Hartley and Tjelvar S.G. Olsson · 2020
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Fastai: A layered api for deep learning
Jeremy Howard and Sylvain Gugger · 2020
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Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2020
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Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
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Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
Cited alongside, same era.
Generative replay with feedback connections as a general strategy for continual learning
Gido M van de Ven and Andreas S Tolias · 2018
Cited alongside, same era.
Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2018
Cited alongside, same era.
Vizdoom competitions: Playing doom from pixels
Marek Wydmuch, Michał Kempka, and Wojciech Jaśkowski · 2018
Cited alongside, same era.
Task-Free Continual Learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
Cited alongside, same era.
Efficient Lifelong Learning with A-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
Cited alongside, same era.
Pytorch lightning
WA Falcon and .al · 2019
Cited alongside, same era.
Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, and Natalia Díaz-Rodríguez · 2020
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Continual Reinforcement Learning in 3D Non-Stationary Environments
Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, and Davide Maltoni · 2020
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Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
Vincenzo Lomonaco, Davide Maltoni, and Lorenzo Pellegrini · 2020
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Class-incremental learning: survey and performance evaluation
Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer · 2020
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Online continual learning on sequences
German I Parisi and Vincenzo Lomonaco · 2020
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Latent replay for real-time continual learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, and Davide Maltoni · 2020
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Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d’Alché Buc, Emily Fox, and Hugo Larochelle · 2020
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Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, V. Larivière, A. Beygelzimer, Florence d’Alché Buc, Emily Fox, and H. Larochelle · 2020
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GDumb: A Simple Approach that Questions Our Progress in Continual Learning
Ameya Prabhu, Philip H. S. Torr, and Puneet K. Dokania · 2020
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Stream-51: Streaming classification and novelty detection from videos
Ryne Roady, Tyler L Hayes, Hitesh Vaidya, and Christopher Kanan · 2020
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The computational limits of deep learning
Neil C Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F Manso · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
Later among the works it cites.
Continual learning for recurrent neural networks: a review and empirical evaluation
Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, and Davide Bacciu · 2021
Closest in time.
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
Closest in time.