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A continual learning agent should be able to build on top of existing knowledge to learn on new data quickly while minimizing forgetting.
A meta-transfer objective for learning to disentangle causal mechanisms
Bengio, Yoshua, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal (2019) · 1901
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Continual learning with tiny episodic memories
Chaudhry, Arslan, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato (2019) · 1902
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Evolutionary principles in self-referential learning, or on learning how to learn
Schmidhuber, Jurgen (1987) · 1987
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Integrated architectures for learning planning and reacting based on approximating dynamic programming
Sutton, Richard (1990) · 1990
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Using semi-distributed representations to overcome catastrophic forgetting in connectionist networks
French, Robert M (1991) · 1991
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Lin, Long-Ji (1992) · 1992
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Catastrophic forgetting in connectionist networks
French, Robert M (1999) · 1999
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Adam: A method for stochastic optimization
Kingma, Diederik P and Jimmy Ba (2014) · 2014
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Human-level concept learning through probabilistic program induction
Lake, Brenden M, Ruslan Salakhutdinov, and Joshua B Tenenbaum (2015) · 2015
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Human-level control through deep reinforcement learning
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. (2015) · 2015
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, Chelsea, Pieter Abbeel, and Sergey Levine (2017) · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, James, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. (2017) · 2017
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Overcoming catastrophic forgetting by incremental moment matching
Lee, Sang-Woo, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang (2017) · 2017
Cited alongside, same era.
Meta-sgd: Learning to learn quickly for few-shot learning
Li, Zhenguo, Fengwei Zhou, Fei Chen, and Hang Li (2017) · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
Lopez-Paz, David and Marc’Aurelio Ranzato (2017) · 2017
Cited alongside, same era.
Learning to Learn with Gradients
Finn, Chelsea (2018, Aug) · 2018
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Learning without forgetting
Li, Zhizhong and Derek Hoiem (2018) · 2018
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Rotate your networks: Better weight consolidation and less catastrophic forgetting
Liu, Xialei, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov (2018) · 2018
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Gradient based sample selection for online continual learning
Aljundi, Rahaf, Min Lin, Baptiste Goujaud, and Yoshua Bengio (2019) · 2019
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Selfless sequential learning
Aljundi, Rahaf, Marcus Rohrbach, and Tinne Tuytelaars (2019) · 2019
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Efficient lifelong learning with a-gem
Chaudhry, Arslan, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny (2019) · 2019
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Rebuffi, Sylvestre-Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert (2017) · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Shin, Hanul, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim (2017) · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Zenke, Friedemann, Ben Poole, and Surya Ganguli (2017) · 2017
Cited alongside, same era.
Continuous adaptation via meta-learning in nonstationary and competitive environments
Al-Shedivat, Maruan, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel (2018) · 2018
Cited alongside, same era.
Memory aware synapses: Learning what (not) to forget
Aljundi, Rahaf, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars (2018) · 2018
Cited alongside, same era.
The utility of sparse representations for control in reinforcement learning
Liu, Vincent, Raksha Kumaraswamy, Lei Le, and Martha White (2019) · 2019
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Meta-learning update rules for unsupervised representation learning
Metz, Luke, Niru Maheswaranathan, Brian Cheung, and Jascha Sohl-dickstein (2019) · 2019
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Deep online learning via meta-learning: Continual adaptation for model-based rl
Nagabandi, Anusha, Chelsea Finn, and Sergey Levine (2019) · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Riemer, Matthew, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro (2019) · 2019
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