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We propose a framework for the completely unsupervised learning of latent object properties from their interactions: the perception-prediction network (PPN).
The recurrent temporal restricted boltzmann machine
Ilya Sutskever, Geoffrey E Hinton, and Graham W Taylor · 2009
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Internal physics models guide probabilistic judgments about object dynamics
Jessica Hamrick, Peter Battaglia, and Joshua B Tenenbaum · 2011
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Simulation as an engine of physical scene understanding
Peter W Battaglia, Jessica B Hamrick, and Joshua B Tenenbaum · 2013
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Learning physics from dynamical scenes
Tomer Ullman, Andreas Stuhlmüller, Noah Goodman, and Joshua B Tenenbaum · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Humans predict liquid dynamics using probabilistic simulation
Christopher Bates, Peter Battaglia, Ilker Yildirim, and Joshua B Tenenbaum · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Jiajun Wu, Ilker Yildirim, Joseph J Lim, Bill Freeman, and Josh Tenenbaum · 2015
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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 Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Physics 101: Learning physical object properties from unlabeled videos
Jiajun Wu, Joseph J Lim, Hongyi Zhang, Joshua B Tenenbaum, and William T Freeman · 2016
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
Cited alongside, same era.
Learning visual predictive models of physics for playing billiards
Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik · 2016
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Learning physical intuition of block towers by example
Adam Lerer, Sam Gross, and Rob Fergus · 2016
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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Learning a physical long-term predictor
Sebastien Ehrhardt, Aron Monszpart, Niloy J Mitra, and Andrea Vedaldi · 2017
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Discovering objects and their relations from entangled scene representations
David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, and Peter Battaglia · 2017
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Visual interaction networks
Nicholas Watters, Andrea Tacchetti, Theophane Weber, Razvan Pascanu, Peter Battaglia, and Daniel Zoran · 2017
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Learning to see physics via visual de-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, and Josh Tenenbaum · 2017
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Cited alongside, same era.
Newtonian scene understanding: Unfolding the dynamics of objects in static images
Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, and Ali Farhadi · 2016
Cited alongside, same era.
“what happens if…” learning to predict the effect of forces in images
Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali Farhadi · 2016
Cited alongside, same era.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
Cited alongside, same era.
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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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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Flexible neural representation for physics prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B Tenenbaum, and Daniel LK Yamins · 2018
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