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Iterative refinement -- start with a random guess, then iteratively improve the guess -- is a useful paradigm for representation learning because it offers a way to break symmetries among equally plausible explanations for the data.
Maximum likelihood from incomplete data via the em algorithm
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
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The Helmholtz machine
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Judea Pearl · 1995
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A view of the EM algorithm that justifies incremental, sparse, and other variants
Radford M Neal and Geoffrey E Hinton · 1998
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
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The free-energy principle: a unified brain theory?
Karl Friston · 2010
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Artificial intelligence a modern approach
Stuart J Russell · 2010
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Self-organization and associative memory , volume 8
Teuvo Kohonen · 2012
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Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
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Using fast weights to attend to the recent past
Jimmy Ba, Geoffrey E Hinton, Volodymyr Mnih, Joel Z Leibo, and Catalin Ionescu · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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A theory of generative convnet
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu · 2016
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Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Neural expectation maximization
Klaus Greff, Sjoerd Van Steenkiste, and Jürgen Schmidhuber · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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High-level library to help with training neural networks in pytorch
V. Fomin, J. Anmol, S. Desroziers, J. Kriss, and A. Tejani · 2020
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On the binding problem in artificial neural networks
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Iterative amortized policy optimization
Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, and Yisong Yue · 2020
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Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 2018
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Shapestacks: Learning vision-based physical intuition for generalised object stacking
Oliver Groth, Fabian B Fuchs, Ingmar Posner, and Andrea Vedaldi · 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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Cooperative training of descriptor and generator networks
Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, and Ying Nian Wu · 2018
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Differentiable convex optimization layers
Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and Zico Kolter · 2019
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Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 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 Tenenbaum, and Sergey Levine · 2020
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Stabilizing equilibrium models by Jacobian regularization
Shaojie Bai, Vladlen Koltun, and J Zico Kolter · 2021
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Fixed point networks: Implicit depth models with Jacobian-free backprop
Samy Wu Fung, Howard Heaton, Qiuwei Li, Daniel McKenzie, Stanley Osher, and Wotao Yin · 2021
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On training implicit models
Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai, Yisen Wang, and Zhouchen Lin · 2021
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Implicit 2 : Implicit layers for implicit representations
Zhichun Huang, Shaojie Bai, and J Zico Kolter · 2021
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Going beyond linear transformers with recurrent fast weight programmers
Kazuki Irie, Imanol Schlag, Róbert Csordás, and Jürgen Schmidhuber · 2021
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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Conditional object-centric learning from video
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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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Step-unrolled denoising autoencoders for text generation
Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, and Aaron van den Oord · 2021
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Illiterate DALL ⋅ \cdot E learns to compose
Gautam Singh, Fei Deng, and Sungjin Ahn · 2021
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Learning energy-based model with variational auto-encoder as amortized sampler
Jianwen Xie, Zilong Zheng, and Ping Li · 2021
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Multiset-equivariant set prediction with approximate implicit differentiation
Yan Zhang, David W Zhang, Simon Lacoste-Julien, Gertjan J Burghouts, and Cees GM Snoek · 2021
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Parts: Unsupervised segmentation with slots, attention and independence maximization
Daniel Zoran, Rishabh Kabra, Alexander Lerchner, and Danilo J Rezende · 2021
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Savi++: Towards end-to-end object-centric learning from real-world videos
Gamaleldin F Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C Mozer, and Thomas Kipf · 2022
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Causal de Finetti: On the identification of invariant causal structure in exchangeable data
Siyuan Guo, Viktor Tóth, Bernhard Schölkopf, and Ferenc Huszár · 2022
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Jianwen Xie, Yaxuan Zhu, Jun Li, and Ping Li · 2022
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