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Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
R. Ratcliff · 1990
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
J. L. McClelland, B. L. McNaughton, and R. C. O’Reilly · 1995
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ANTHONY ROBINS · 1995
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
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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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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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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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Overcoming catastrophic forgetting by incremental moment matching
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Gradient episodic memory for continual learning
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Averaging weights leads to wider optima and better generalization
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2018
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Progress & compress: A scalable framework for continual learning
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Progress and compress: A scalable framework for continual learning
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Efficient lifelong learning with a-gem
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Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
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Michael Zhang, James Lucas, Jimmy Ba, and Geoffrey E Hinton · 2019
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Sharpness-aware minimization for efficiently improving generalization
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An empirical investigation of the role of pre-training in lifelong learning
Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, and Emma Strubell · 2021
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Dualnet: Continual learning, fast and slow
Quang Pham, Chenghao Liu, and Steven Hoi · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Dark experience for general continual learning: a strong, simple baseline
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Clinical applications of continual learning machine learning
Cecilia Lee and Aaron Lee · 2020
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Dropout as an implicit gating mechanism for continual learning
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Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
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Understanding the role of training regimes in continual learning
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Gdumb: A simple approach that questions our progress in continual learning
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip Torr, and Puneet Dokania · 2020
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Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima
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Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima
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Scaling the number of tasks in continual learning, 2022
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