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Visual environments are structured, consisting of distinct objects or entities.
Entity abstraction in visual model-based reinforcement learning
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Interaction networks for learning about objects, relations and physics
Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and Koray Kavukcuoglu · 2016
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Attend, infer, repeat: Fast scene understanding with generative models
SM Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, and Geoffrey E Hinton · 2016
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Tagger: Deep unsupervised perceptual grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber, and Harri Valpola · 2016
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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 · 2016
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Categorical reparameterization with gumbel-softmax
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Yoshua Bengio · 2017
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Geometric deep learning: going beyond euclidean data
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Neural task programming: Learning to generalize across hierarchical tasks
Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, and Silvio Savarese · 2018
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Monet: Unsupervised scene decomposition and representation
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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 · 2019
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Making sense of sensory input
Richard Evans, José Hernández-Orallo, Johannes Welbl, Pushmeet Kohli, and Marek Sergot · 2019
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Recurrent independent mechanisms, 2019
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, and Bernhard Schölkopf · 2019
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Making neural programming architectures generalize via recursion
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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Discovering objects and their relations from entangled scene representations
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Routing networks: Adaptive selection of non-linear functions for multi-task learning
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Visual interaction networks: Learning a physics simulator from video
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Contrastive learning of structured world models
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Routing networks and the challenges of modular and compositional computation
Clemens Rosenbaum, Ignacio Cases, Matthew Riemer, and Tim Klinger · 2019
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Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
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Unsupervised discovery of 3d physical objects from video
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Inductive biases for deep learning of higher-level cognition
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Strong generalization and efficiency in neural programs
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S2rms: Spatially structured recurrent modules
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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, et al · 2020
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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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Unmasking the inductive biases of unsupervised object representations for video sequences, 2020
Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, and Alexander S. Ecker · 2020
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Unsupervised video decomposition using spatio-temporal iterative inference
Polina Zablotskaia, Edoardo A Dominici, Leonid Sigal, and Andreas M Lehrmann · 2020
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
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