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Many current methods to learn intuitive physics are based on interaction networks and similar approaches.
Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
Earlier work this paper cites.
Simulation as an engine of physical scene understanding
Peter W. Battaglia, Jessica B. Hamrick, and Joshua B. Tenenbaum · 2013
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Learning physical intuition of block towers by example
Adam Lerer, Sam Gross, and Rob Fergus · 2016
Earlier work this paper cites.
Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
Earlier work this paper cites.
Learning a physical long-term predictor
Sebastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, and Andrea Vedaldi · 2017
Earlier work this paper cites.
A compositional object-based approach to learning physical dynamics
Michael B. Chang, Tomer Ullman, Antonio Torralba, and Joshua B. Tenenbaum · 2017
Earlier work this paper cites.
Neural scene de-rendering
Jiajun Wu, Joschua B. Tenenbaum, and Pushmeet Kohli · 2017
Cited alongside, same era.
Mind games: Game engines as an architecture for intuitive physics
Tomer D. Ullman, Elizabeth Spelke, Peter Battaglia, and Joshua B. Tenenbaum · 2017
Cited alongside, same era.
Discovering objects and their relations from entangled scene representations
David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, and Peter Battaglia · 2017
Cited alongside, same era.
Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Andrea Tacchetti, Theophane Weber, Razvan Pascanu, Peter Battaglia, and Daniel Zoran · 2017
Cited alongside, same era.
Metacontrol for adaptive imagination-based optimization
Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, and Peter W. Battaglia · 2017
Cited alongside, same era.
Discovering physical concepts with neural networks
Raban Iten, Tony Metger, Henrik Wilming, Lidia del Rio, and Renato Renner · 2018
Later among the works it cites.
Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Sjoerd van Steenkiste, Michael Chang, Klaus Greff, and Jürgen Schmidhuber · 2018
Later among the works it cites.
Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
Later among the works it cites.
Unsupervised learning of latent physical properties using perception-prediction networks
David Zheng, Vinson Luo, Jiajun Wu, and Joshua B. Tenenbaum · 2018
Later among the works it cites.
Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
Later among the works it cites.
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Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sebastien Racanière, David Reichert, Théophane Weber, Daan Wierstra, and Peter Battaglia · 2017
Cited alongside, same era.
A neural-symbolic architecture for inverse graphics improved by lifelong meta-learning
Michael Kissner and Helmut Mayer · 2019
Closest in time.