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Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution.
Evolutionary principles in self-referential learning
Jurgen Schmidhuber · 1987
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Learning a synaptic learning rule
Yoshua Bengio, Samy Bengio, and Jocelyn Cloutier · 1990
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A framework for the cooperation of learning algorithms
Léon Bottou and Patrick Gallinari · 1991
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Adaptive mixtures of local experts
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Modular neural networks and self-decomposition
Eric Ronco, Henrik Gollee, and Peter J Gawthrop · 1997
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Neural machine translation by jointly learning to align and translate
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Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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RL 2 \text{RL}^{2} : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Optimization as a model for few-shot learning
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Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Causal and statistical learning
B. Schölkopf, D. Janzing, and D. Lopez-Paz · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, and Ruslan Salakhutdinov · 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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Grounded language learning in a simulated 3d world
Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, et al · 2017
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Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Judy Hoffman, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Zhongwen Xu, Hado P van Hasselt, and David Silver · 2018
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A meta-transfer objective for learning to disentangle causal mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal · 2019
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Reinforcement learning, fast and slow
Matthew Botvinick, Sam Ritter, Jane X Wang, Zeb Kurth-Nelson, Charles Blundell, and Demis Hassabis · 2019
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Jeff Clune · 2019
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Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Attention is all you need
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Ferran Alet, Tomás Lozano-Pérez, and Leslie P Kaelbling · 2018
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Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, and Aaron Courville · 2018
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Sparse attentive backtracking: Temporal credit assignment through reminding
Nan Rosemary Ke, Anirudh Goyal ALIAS PARTH GOYAL, Olexa Bilaniuk, Jonathan Binas, Michael C Mozer, Chris Pal, and Yoshua Bengio · 2018
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Recurrent independent mechanisms
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Louis Kirsch, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2019
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Routing networks and the challenges of modular and compositional computation
Clemens Rosenbaum, Ignacio Cases, Matthew Riemer, and Tim Klinger · 2019
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Inductive biases for deep learning of higher-level cognition
Anirudh Goyal and Yoshua Bengio · 2020
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Object files and schemata: Factorizing declarative and procedural knowledge in dynamical systems
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Discovering reinforcement learning algorithms
Junhyuk Oh, Matteo Hessel, Wojciech M Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, and David Silver · 2020
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S2rms: Spatially structured recurrent modules
Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, and Bernhard Schölkopf · 2020
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Coordination among neural modules through a shared global workspace
Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, and Yoshua Bengio · 2021
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