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A central challenge in developing versatile machine learning systems is catastrophic forgetting: a model trained on tasks in sequence will suffer significant performance drops on earlier tasks.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
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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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Concept formation in infancy
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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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Overcoming catastrophic forgetting in neural networks
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Overcoming catastrophic forgetting by incremental moment matching
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Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serrà, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Online structured laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
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Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler L Hayes, and Christopher Kanan · 2018
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Selective experience replay for lifelong learning
David Isele and Akansel Cosgun · 2018
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Ari Morcos, Maithra Raghu, and Samy Bengio · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Exploring the limits of transfer learning with a unified text-to-text transformer
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
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Chiyuan Zhang, Samy Bengio, and Yoram Singer · 2019
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Superposition of many models into one
Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, and Bruno Olshausen · 2019
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Continual learning via neural pruning
Siavash Golkar, Michael Kagan, and Kyunghyun Cho · 2019
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Uncertainty-guided continual learning with bayesian neural networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
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Do imagenet classifiers generalize to imagenet?
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Toward understanding catastrophic forgetting in continual learning
Cuong V Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan, and Stefano Soatto · 2019
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Does an lstm forget more than a cnn? an empirical study of catastrophic forgetting in nlp
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A mathematical theory of semantic development in deep neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2019
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Wider networks learn better features, 2019
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A deep learning approach to antibiotic discovery
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