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Machine learning systems, especially with overparameterized deep neural networks, can generalize to novel test instances drawn from the same distribution as the training data.
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Wenshuai Zhao, Jorge Peña Queralta, and Tomi Westerlund · 2020
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Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, and Kilian Weinberger · 2017
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Attention is all you need
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Relational inductive biases, deep learning, and graph networks
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Combinatorial optimization and reasoning with graph neural networks
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Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, and Rose Yu · 2021
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Vector neurons: A general framework for SO(3)-equivariant networks
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas J Guibas · 2021
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Why generalization in RL is difficult: Epistemic POMDPs and implicit partial observability
Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, and Sergey Levine · 2021
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Bi-linear value networks for multi-goal reinforcement learning
Zhang-Wei Hong, Ge Yang, and Pulkit Agrawal · 2021
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Visual analogy: Deep learning versus compositional models
Nicholas Ichien, Qing Liu, Shuhao Fu, Keith J Holyoak, Alan Yuille, and Hongjing Lu · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
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A survey of generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2021
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WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran S. Haque, Sara M. Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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Abstraction and analogy-making in artificial intelligence
Melanie Mitchell · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Euclidean symmetry and equivariance in machine learning
Tess E Smidt · 2021
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Inductive biases and variable creation in self-attention mechanisms
Benjamin L Edelman, Surbhi Goel, Sham Kakade, and Cyril Zhang · 2022
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Bisimulation makes analogies in goal-conditioned reinforcement learning
Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, and Sergey Levine · 2022
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Theory of graph neural networks: Representation and learning
Stefanie Jegelka · 2022
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Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al · 2022
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Neural descriptor fields: SE(3)-equivariant object representations for manipulation
Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann · 2022
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Multi-task pre-training for plug-and-play task-oriented dialogue system
Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai, and Yi Zhang · 2022
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SO(2)-equivariant reinforcement learning
Dian Wang, Robin Walters, and Robert Platt · 2022
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Policy architectures for compositional generalization in control
Allan Zhou, Vikash Kumar, Chelsea Finn, and Aravind Rajeswaran · 2022
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