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In disentangled representation learning, a model is asked to tease apart a dataset's underlying sources of variation and represent them independently of one another.
Independent component analysis, a new concept?
Pierre Comon · 1994
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Nonlinear independent component analysis: existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
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Independent component analysis: algorithms and applications
Aapo Hyvärinen and Erkki Oja · 2000
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Cluster ensembles—a knowledge reuse framework for combining multiple partitions
Alexander Strehl and Joydeep Ghosh · 2002
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Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger · 2004
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Matplotlib: a 2D graphics environment
John D Hunter · 2007
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Scikit-learn: machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2011
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Deep learning of representations: looking forward
Yoshua Bengio · 2013
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Mutual information between discrete and continuous data sets
Brian C Ross · 2014
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An extension of slow feature analysis for nonlinear blind source separation
Henning Sprekeler, Tiziano Zito, and Laurenz Wiskott · 2014
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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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Auditing for transparency in content personalization systems
Brent Mittelstadt · 2016
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A survey of inductive biases for factorial representation-learning
Karl Ridgeway · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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End-to-end optimized image compression
Johannes Ballé, Valero Laparra, and Eero P. Simoncelli · 2017
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Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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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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Neural discrete representation learning
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
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Lossy image compression with compressive autoencoders
Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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3D shapes dataset, 2018
Chris Burgess and Hyunjik Kim · 2018
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Isolating sources of disentanglement in variational autoencoders
Ricky TQ 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 KI Williams · 2018
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Demystifying fixed k k -nearest neighbor information estimators
Weihao Gao, Sewoong Oh, and Pramod Viswanath · 2018
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Fast decoding in sequence models using discrete latent variables
Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, and Noam Shazeer · 2018
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Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 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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Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel · 2019
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Techniques for interpretable machine learning
Mengnan Du, Ninghao Liu, and Xia Hu · 2019
A theory of usable information under computational constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, and Stefano Ermon · 2020
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Independent mechanism analysis, a new concept?
Luigi Gresele, Julius Von Kügelgen, Vincent Stimper, Bernhard Schölkopf, and Michel Besserve · 2021
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When is unsupervised disentanglement possible?
Daniella Horan, Eitan Richardson, and Yair Weiss · 2021
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Equinox: neural networks in JAX via callable PyTrees and filtered transformations
Patrick Kidger and Cristian Garcia · 2021
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Towards nonlinear disentanglement in natural data with temporal sparse coding
David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dylan Paiton · 2021
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Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging
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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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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High resolution disentanglement datasets, 2019
Weili Nie · 2019
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Quantifying interpretability and trust in machine learning systems
Philipp Schmidt and Felix Biessmann · 2019
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Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Mototaka Miyake, Masamichi Takahashi, Akiko Nakagawa, Tatsuya Harada, and Ryuji Hamamoto · 2021
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Explanation-based human debugging of NLP models: a survey
Piyawat Lertvittayakumjorn and Francesca Toni · 2021
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Discrete-valued neural communication
Dianbo Liu, Alex M Lamb, Kenji Kawaguchi, Anirudh Goyal ALIAS PARTH GOYAL, Chen Sun, Michael C Mozer, and Yoshua Bengio · 2021
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Anomaly detection through latent space restoration using vector quantized variational autoencoders
Sergio Naval Marimont and Giacomo Tarroni · 2021
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Jacob Walker, Ali Razavi, and Aäron van den Oord · 2021
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Seaborn: statistical data visualization
Michael L Waskom · 2021
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VideoGPT: video generation using VQ-VAE and transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas · 2021
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AudioLM: a language modeling approach to audio generation
Zalán Borsos, Raphaël Marinier, Damien Vincent, Eugene Kharitonov, Olivier Pietquin, Matt Sharifi, Olivier Teboul, David Grangier, Marco Tagliasacchi, and Neil Zeghidour · 2022
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Function classes for identifiable nonlinear independent component analysis
Simon Buchholz, Michel Besserve, and Bernhard Schölkopf · 2022
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Measuring disentanglement: a review of metrics
Marc-André Carbonneau, Julian Zaidi, Jonathan Boilard, and Ghyslain Gagnon · 2022
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Identifiability of deep generative models without auxiliary information
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, and Bryon Aragam · 2022
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Disentanglement via mechanism sparsity regularization: a new principle for nonlinear ICA
Sebastien Lachapelle, Pau Rodriguez, Yash Sharma, Katie E Everett, Rémi Le Priol, Alexandre Lacoste, and Simon Lacoste-Julien · 2022
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Identifiable deep generative models via sparse decoding
Gemma Elyse Moran, Dhanya Sridhar, Yixin Wang, and David Blei · 2022
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Disentanglement of correlated factors via hausdorff factorized support
Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, and Diane Bouchacourt · 2022
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Nonlinear ICA using volume-preserving transformations
Xiaojiang Yang, Yi Wang, Jiacheng Sun, Xing Zhang, Shifeng Zhang, Zhenguo Li, and Junchi Yan · 2022
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On the identifiability of nonlinear ICA: sparsity and beyond
Yujia Zheng, Ignavier Ng, and Kun Zhang · 2022
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Dci-es: An extended disentanglement framework with connections to identifiability
Cian Eastwood, Andrei Liviu Nicolicioiu, Julius Von Kügelgen, Armin Kekić, Frederik Träuble, Andrea Dittadi, and Bernhard Schölkopf · 2023
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Structure by architecture: structured representations without regularization
Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, and Bernhard Schölkopf · 2023
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Discrete key-value bottleneck
Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, and Bernhard Schölkopf · 2023
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Disentanglement with biological constraints: A theory of functional cell types
James C R Whittington, Will Dorrell, Surya Ganguli, and Timothy Behrens · 2023
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Indeterminacy in generative models: characterization and strong identifiability
Quanhan Xi and Benjamin Bloem-Reddy · 2023
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