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Neural networks leverage robust internal representations in order to generalise.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Interaction networks for learning about objects, relations and physics
Peter W Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, and Koray Kavukcuoglu · 2016
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2017
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Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
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The power and limits of deep learning
Yann LeCun · 2018
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David Ha and Jürgen Schmidhuber · 2018
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Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Sjoerd Van Steenkiste, Michael Chang, Klaus Greff, and Jürgen Schmidhuber · 2018
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Sequential attend, infer, repeat: Generative modelling of moving objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
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Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
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Relational recurrent neural networks
Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, and Timothy Lillicrap · 2018
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Relational inductive bias for physical construction in humans and machines
Jessica B Hamrick, Kelsey R Allen, Victor Bapst, Tina Zhu, Kevin R McKee, Joshua B Tenenbaum, and Peter W Battaglia · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, and Skye Wanderman-Milne · 2018
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
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What can neural networks reason about?
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2019
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Neural execution of graph algorithms
Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell · 2019
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Neural shuffle-exchange networks-sequence processing in o (n log n) time
Karlis Freivalds, Emīls Ozoliņš, and Agris Šostaks · 2019
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Hamiltonian graph networks with ode integrators
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, and Peter Battaglia · 2019
Cited alongside, same era.
Combined reinforcement learning via abstract representations
Entity abstraction in visual model-based reinforcement learning
Rishi Veerapaneni, John D Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua Tenenbaum, and Sergey Levine · 2020
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Scalor: Generative world models with scalable object representations
Jindong Jiang, Sepehr Janghorbani, Gerard De Melo, and Sungjin Ahn · 2020
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Improving generative imagination in object-centric world models
Zhixuan Lin, Yi-Fu Wu, Skand Peri, Bofeng Fu, Jindong Jiang, and Sungjin Ahn · 2020
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Contrastive learning of structured world models
Thomas Kipf, Elise van der Pol, and Max Welling · 2020
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Causal discovery in physical systems from videos
Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, and Animesh Garg · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Vincent François-Lavet, Yoshua Bengio, Doina Precup, and Joelle Pineau · 2019
Cited alongside, same era.
Unsupervised learning of object keypoints for perception and control
Tejas D Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, and Volodymyr Mnih · 2019
Cited alongside, same era.
Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Nicholas Watters, Loic Matthey, Christopher P Burgess, and Alexander Lerchner · 2019
Cited alongside, same era.
Unsupervised state representation learning in atari
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, and R Devon Hjelm · 2019
Cited alongside, same era.
Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, and David Silver · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David Jones, David Silver, Koray Kavukcuoglu, and Demis Hassabis · 2020
Cited alongside, same era.
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On the binding problem in artificial neural networks
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2020
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Learning object-centric video models by contrasting sets
Sindy Löwe, Klaus Greff, Rico Jonschkowski, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Agent57: Outperforming the atari human benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, and Charles Blundell · 2020
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Haiku: Sonnet for JAX, 2020
Tom Hennigan, Trevor Cai, Tamara Norman, and Igor Babuschkin · 2020
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Neural algorithmic reasoners are implicit planners
Andreea-Ioana Deac, Petar Veličković, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, and Mladen Nikolic · 2021
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Neural algorithmic reasoning
Petar Veličković and Charles Blundell · 2021
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Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, and Petar Veličković · 2021
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Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell, and Petar Veličković · 2021
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How to transfer algorithmic reasoning knowledge to learn new algorithms?
Louis-Pascal Xhonneux, Andreea-Ioana Deac, Petar Veličković, and Jian Tang · 2021
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Learning mesh-based simulation with graph networks
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W Battaglia · 2021
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gradsim: Differentiable simulation for system identification and visuomotor control
Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jerome Parent-Levesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, and Sanja Fidler · 2021
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How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2021
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