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The key to high-level cognition is believed to be the ability to systematically manipulate and compose knowledge pieces.
Recurrent independent mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, and Bernhard Schölkopf · 1909
Earlier work this paper cites.
Exploiting spatial invariance for scalable unsupervised object tracking
Eric Crawford and Joelle Pineau · 1911
Earlier work this paper cites.
Connectionism and cognitive architecture: A critical analysis
Jerry A Fodor and Zenon W Pylyshyn · 1988
Earlier work this paper cites.
Schema theory revisited
Mary B McVee, Kailonnie Dunsmore, and James R Gavelek · 2005
Earlier work this paper cites.
Unsupervised discovery of 3d physical objects from video
Yilun Du, Kevin A. Smith, Tomer Ulman, Joshua B. Tenenbaum, and Jiajun Wu · 2007
Earlier work this paper cites.
On the binding problem in artificial neural networks
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2012
Earlier work this paper cites.
On the binding problem in artificial neural networks
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2012
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From machine learning to machine reasoning
Léon Bottou · 2014
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Attend, infer, repeat: Fast scene understanding with generative models
SM Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, and Geoffrey E Hinton · 2016
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Tagger: Deep unsupervised perceptual grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, and Jürgen Schmidhuber · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Neural expectation maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew M Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Earlier work this paper cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Earlier work this paper cites.
Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
Earlier work this paper cites.
Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu · 2017
Earlier work this paper cites.
Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher K. I. Williams · 2018
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Hyunjik Kim and Andriy Mnih · 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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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2018
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Learning deep disentangled embeddings with the f-statistic loss
Karl Ridgeway and Michael C. Mozer · 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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Measuring compositionality in representation learning
Jacob Andreas · 2019
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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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Genesis: Generative scene inference and sampling with object-centric latent representations, 2019
Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2019
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufmann, Rishab Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Tracking by animation: Unsupervised learning of multi-object attentive trackers
Zhen He, Jian Li, Daxue Liu, Hangen He, and David Barber · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Roots: Object-centric representation and rendering of 3d scenes
Chang Chen, Fei Deng, and Sungjin Ahn · 2021
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Unsupervised object-based transition models for 3d partially observable environments
Antonia Creswell, Rishabh Kabra, Chris Burgess, and Murray Shanahan · 2021
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Genesis-v2: Inferring unordered object representations without iterative refinement
Martin Engelcke, Oiwi Parker Jones, and Ingmar Posner · 2021
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Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew Botvinick, Alexander Lerchner, and Christopher P Burgess · 2021
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Aleksandar Stanić and Jürgen Schmidhuber · 2019
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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 B Tenenbaum, and Sergey Levine · 2019
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Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P Burgess, and Alexander Lerchner · 2019
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Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Nicholas Watters, Loic Matthey, Christopher P. Burgess, and Alexander Lerchner · 2019
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Parts: Unsupervised segmentation with slots, attention and independence maximization
Daniel Zoran, Rishabh Kabra, Alexander Lerchner, and Danilo J Rezende · 2019
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Object-centric image generation with factored depths, locations, and appearances
Titas Anciukevicius, Christoph H Lampert, and Paul Henderson · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Learning 3d object-oriented world models from unlabeled videos
Eric Crawford and Joelle Pineau · 2020
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Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, and Klaus Greff · 2021
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Transformers with competitive ensembles of independent mechanisms
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Discrete-valued neural communication
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The role of disentanglement in generalisation
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers · 2021
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Disentangling 3d prototypical networks for few-shot concept learning
Mihir Prabhudesai, Shamit Lal, Darshan Patil, Hsiao-Yu Fish Tung, Adam W. Harley, and Katerina Fragkiadaki · 2021
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Hopfield networks is all you need
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Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Structured world belief for reinforcement learning in pomdp
Gautam Singh, Skand Peri, Junghyun Kim, Hyunseok Kim, and Sungjin Ahn · 2021
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Interactive disentanglement: Learning concepts by interacting with their prototype representations
Wolfgang Stammer, Marius Memmel, Patrick Schramowski, and Kristian Kersting · 2021
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Decomposing 3d scenes into objects via unsupervised volume segmentation
Karl Stelzner, Kristian Kersting, and Adam R Kosiorek · 2021
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Generative video transformer: Can objects be the words?
Yi-Fu Wu, Jaesik Yoon, and Sungjin Ahn · 2021
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Unsupervised foreground extraction via deep region competition
Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, and Song-Chun Zhu · 2021
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Towards self-supervised learning of global and object-centric representations
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Generalization and robustness implications in object-centric learning
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Savi++: Towards end-to-end object-centric learning from real-world videos
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