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The ability to perceive and reason about individual objects and their interactions is a goal to be achieved for building intelligent artificial systems.
Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 1903
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Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P Burgess, and Alexander Lerchner · 1905
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Genesis: Generative scene inference and sampling with object-centric latent representations
Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 1907
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Contrastive learning of structured world models, 2019
Thomas Kipf, Elise van der Pol, and Max Welling · 1911
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Learning first-order markov models for control
Pieter Abbeel and Andrew Ng · 2004
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Object-centric learning with slot attention, 2020
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2006
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Core knowledge
Elizabeth S Spelke and Katherine D Kinzler · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Model regularization for stable sample rollouts
Erik Talvitie · 2014
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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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A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
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Vain: Attentional multi-agent predictive modeling
Yedid Hoshen · 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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Relational inductive biases, deep learning, and graph networks
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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Nervenet: Learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
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Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Unsupervised discovery of parts, structure, and dynamics
Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T Freeman, Joshua B Tenenbaum, and Jiajun Wu · 2020
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Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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David Ha and Jürgen Schmidhuber · 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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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
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Spriteworld: A flexible, configurable reinforcement learning environment
Nicholas Watters, Loic Matthey, Sebastian Borgeaud, Rishabh Kabra, and Alexander Lerchner
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Michael Chang, Thomas L. Griffiths, and Sergey Levine · 2022
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Improving object-centric learning with query optimization, 2022
Baoxiong Jia, Yu Liu, and Siyuan Huang · 2022
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Invariant slot attention: Object discovery with slot-centric reference frames
Ondrej Biza, Sjoerd van Steenkiste, Mehdi SM Sajjadi, Gamaleldin F Elsayed, Aravindh Mahendran, and Thomas Kipf · 2023
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Model-based reinforcement learning: A survey
Thomas M Moerland, Joost Broekens, Aske Plaat, Catholijn M Jonker, et al · 2023
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