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In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation.
A value for n-person games
Lloyd S Shapley · 1953
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Independent component analysis, a new concept?
Pierre Comon · 1994
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A new learning algorithm for blind signal separation
Shun-ichi Amari, Andrzej Cichocki, Howard Hua Yang, et al · 1996
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information
Howard Hua Yang and Shun-ichi Amari · 1997
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Nonlinear independent component analysis: Existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
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Random forests
Leo Breiman · 2001
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction , volume 2
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Deep convolutional inverse graphics network
Tejas D Kulkarni, William F Whitney, Pushmeet Kohli, and Josh Tenenbaum · 2015
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Deep visual analogy-making
Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 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
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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β \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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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Isolating sources of disentanglement in vaes
Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness
Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf, and Stefan Bauer · 2019
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Understanding global feature contributions with additive importance measures
Ian Covert, Scott M Lundberg, and Su-In Lee · 2020
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In search of robust measures of generalization
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, and Daniel M Roy · 2020
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Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Ricky TQ Chen, Xuechen Li, Roger Grosse, and David 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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Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 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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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
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Variational autoencoders and nonlinear ica: A unifying framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen · 2020
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A sober look at the unsupervised learning of disentangled representations and their evaluation
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2020
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Weakly supervised disentanglement with guarantees
Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Evaluating representations by the complexity of learning low-loss predictors
William F Whitney, Min Jae Song, David Brandfonbrener, Jaan Altosaar, and Kyunghyun Cho · 2020
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Are wider nets better given the same number of parameters?
Anna Golubeva, Guy Gur-Ari, and Behnam Neyshabur · 2021
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Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever · 2021
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The shape of learning curves: a review
Tom Viering and Marco Loog · 2021
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The role of pretrained representations for the OOD generalization of RL agents
Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2022
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