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Variational Autoencoders and Nonlinear ICA: A Unifying Framework

International Conference on Artificial Intelligence and Statistics (AISTATS), 2019

10 July 2019

Papers citing "Variational Autoencoders and Nonlinear ICA: A Unifying Framework"

50 / 402 papers shown

Title
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation TransferNeural Information Processing Systems (NeurIPS), 2020 Zidi Xiu Junya Chen Ricardo Henao B. Goldstein Lawrence Carin Chenyang Tao 143 11 0 25 Nov 2020
Predictive Coding, Variational Autoencoders, and Biological ConnectionsNeural Computation (Neural Comput.), 2019 Joseph Marino DRL AI4CE 228 49 0 15 Nov 2020
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE Ding Zhou Xue-Xin Wei DRL 379 97 0 09 Nov 2020
Causal Autoregressive Flows Ilyes Khemakhem R. Monti R. Leech Aapo Hyvarinen CML OOD AI4CE 349 126 0 04 Nov 2020
Latent Causal Invariant Model Xinwei Sun Botong Wu Xiangyu Zheng Yu Xie Wei Chen Tao Qin Tie-Yan Liu OOD CML BDL 388 14 0 04 Nov 2020
Learning Causal Semantic Representation for Out-of-Distribution Prediction Yu Xie Xinwei Sun Yongfeng Zhang Haoyue Tang Tao Li Tao Qin Wei Chen Tie-Yan Liu CML OODD OOD 594 118 0 03 Nov 2020
On the Transfer of Disentangled Representations in Realistic SettingsInternational Conference on Learning Representations (ICLR), 2020 Andrea Dittadi Frederik Trauble Francesco Locatello M. Wuthrich Vaibhav Agrawal Ole Winther Stefan Bauer Bernhard Schölkopf OOD 354 86 0 27 Oct 2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their EvaluationJournal of machine learning research (JMLR), 2020 Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 170 77 0 27 Oct 2020
Product Manifold LearningInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 Sharon Zhang Amit Moscovich A. Singer 209 17 0 19 Oct 2020
An Identifiable Double VAE For Disentangled Representations Graziano Mita Maurizio Filippone Pietro Michiardi CoGe DRL 230 0 0 19 Oct 2020
Geometric Disentanglement by Random Convex Polytopes M. Joswig M. Kaluba Lukas Ruff 157 4 0 29 Sep 2020
Synbols: Probing Learning Algorithms with Synthetic DatasetsNeural Information Processing Systems (NeurIPS), 2020 Alexandre Lacoste Pau Rodríguez Frederic Branchaud-Charron Parmida Atighehchian Massimo Caccia I. Laradji Alexandre Drouin Matt Craddock Laurent Charlin David Vázquez 180 14 0 14 Sep 2020
Multilinear Latent Conditioning for Generating Unseen Attribute CombinationsInternational Conference on Machine Learning (ICML), 2020 Markos Georgopoulos Grigorios G. Chrysos Maja Pantic Yannis Panagakis GAN DRL 164 18 0 09 Sep 2020
The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement William S. Peebles John Peebles Jun-Yan Zhu Alexei A. Efros Antonio Torralba 208 119 0 24 Aug 2020
Generative chemistry: drug discovery with deep learning generative models Yuemin Bian X. Xie AI4CE 310 127 0 20 Aug 2020
Unifying supervised learning and VAEs -- coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructions T. Glüsenkamp 207 3 0 13 Aug 2020
Nonlinear ISA with Auxiliary Variables for Learning Speech RepresentationsInterspeech (Interspeech), 2020 Amrith Rajagopal Setlur Barnabás Póczós A. Black 73 1 0 25 Jul 2020
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding David A. Klindt Lukas Schott Yash Sharma Ivan Ustyuzhaninov Wieland Brendel Matthias Bethge Dylan M. Paiton CML 270 145 0 21 Jul 2020
Time Series Source Separation with Slow Flows Edouard Pineau S. Razakarivony Thomas Bonald BDL AI4TS 131 2 0 20 Jul 2020
Autoregressive flow-based causal discovery and inference R. Monti Ilyes Khemakhem Aapo Hyvarinen CML 112 4 0 18 Jul 2020
Unsupervised Controllable Generation with Self-TrainingIEEE International Joint Conference on Neural Network (IJCNN), 2020 Grigorios G. Chrysos Jean Kossaifi Zhiding Yu Anima Anandkumar GAN 100 4 0 17 Jul 2020
Identifying Latent Stochastic Differential EquationsIEEE Transactions on Signal Processing (TSP), 2020 Ali Hasan João M. Pereira Sina Farsiu Vahid Tarokh DiffM 247 21 0 12 Jul 2020
On Linear Identifiability of Learned Representations Geoffrey Roeder Luke Metz Diederik P. Kingma CML 371 93 0 01 Jul 2020
Deconvolution with unknown noise distribution is possible for multivariate signals Elisabeth Gassiat Sylvain Le Corff Luc Lehéricy 176 12 0 25 Jun 2020
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series Hermanni Hälvä Aapo Hyvarinen OOD BDL CML 238 85 0 22 Jun 2020
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process H. Morioka Hermanni Hälvä Aapo Hyvarinen 173 26 0 19 Jun 2020
On Disentangled Representations Learned From Correlated DataInternational Conference on Machine Learning (ICML), 2020 Frederik Trauble Elliot Creager Niki Kilbertus Francesco Locatello Andrea Dittadi Anirudh Goyal Bernhard Schölkopf Stefan Bauer OOD CML 231 127 0 14 Jun 2020
An Improved Semi-Supervised VAE for Learning Disentangled Representations Weili Nie Zichao Wang Ankit B. Patel Richard G. Baraniuk CoGe DRL 130 4 0 12 Jun 2020
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMCInternational Conference on Learning Representations (ICLR), 2020 Erik Nijkamp Ruiqi Gao Pavel Sountsov Srinivas Vasudevan Bo Pang Song-Chun Zhu Ying Nian Wu BDL 160 24 0 12 Jun 2020
A Deeper Look at the Unsupervised Learning of Disentangled Representations in $β$ -VAE from the Perspective of Core Object Recognition Harshvardhan Digvijay Sikka OCL OOD BDL DRL 122 1 0 25 Apr 2020
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder Mengyue Yang Furui Liu Zhitang Chen Xinwei Shen Jianye Hao Jun Wang OOD CoGe CML 457 58 0 18 Apr 2020
A theory of independent mechanisms for extrapolation in generative modelsAAAI Conference on Artificial Intelligence (AAAI), 2020 M. Besserve Rémy Sun Dominik Janzing Bernhard Schölkopf 235 26 0 01 Apr 2020
Unshuffling Data for Improved GeneralizationIEEE International Conference on Computer Vision (ICCV), 2020 Damien Teney Ehsan Abbasnejad Anton Van Den Hengel OOD 209 82 0 27 Feb 2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICANeural Information Processing Systems (NeurIPS), 2020 Ilyes Khemakhem R. Monti Diederik P. Kingma Aapo Hyvarinen CML 344 130 0 26 Feb 2020
Learning Bijective Feature Maps for Linear ICAInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 A. Camuto M. Willetts Brooks Paige Chris Holmes Stephen J. Roberts 192 3 0 18 Feb 2020
Few-shot Domain Adaptation by Causal Mechanism TransferInternational Conference on Machine Learning (ICML), 2020 Takeshi Teshima Issei Sato Masashi Sugiyama OOD CML TTA 212 95 0 10 Feb 2020
Weakly-Supervised Disentanglement Without CompromisesInternational Conference on Machine Learning (ICML), 2020 Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 601 346 0 07 Feb 2020
On Implicit Regularization in $β$ -VAEsInternational Conference on Machine Learning (ICML), 2020 Abhishek Kumar Ben Poole DRL 574 58 0 31 Jan 2020
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)International Conference on Learning Representations (ICLR), 2020 Peter Sorrenson Carsten Rother Ullrich Kothe DRL CML 169 125 0 14 Jan 2020
High-Fidelity Synthesis with Disentangled RepresentationEuropean Conference on Computer Vision (ECCV), 2020 Wonkwang Lee Donggyun Kim Seunghoon Hong Honglak Lee DRL 162 68 0 13 Jan 2020
WICA: nonlinear weighted ICA Andrzej Bedychaj Przemysław Spurek A. Nowak Jacek Tabor CML 96 1 0 13 Jan 2020
A Closer Look at Disentangling in $β$ -VAEAsilomar Conference on Signals, Systems and Computers (ACSSC), 2019 Harshvardhan Digvijay Sikka Weishun Zhong J. Yin Cengiz Pehlevan CML CoGe BDL DRL 99 20 0 11 Dec 2019
No Representation without Transformation Giorgio Giannone Saeed Saremi Jonathan Masci Christian Osendorfer BDL DRL 136 2 0 09 Dec 2019
Flow Contrastive Estimation of Energy-Based ModelsComputer Vision and Pattern Recognition (CVPR), 2019 Ruiqi Gao Erik Nijkamp Diederik P. Kingma Zhen Xu Andrew M. Dai Ying Nian Wu GAN 333 120 0 02 Dec 2019
Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent VariablesJournal of machine learning research (JMLR), 2019 Yaniv Yacoby Weiwei Pan Finale Doshi-Velez BDL UQCV 367 1 0 01 Nov 2019
Robust contrastive learning and nonlinear ICA in the presence of outliersConference on Uncertainty in Artificial Intelligence (UAI), 2019 Hiroaki Sasaki Takashi Takenouchi R. Monti Aapo Hyvarinen CML OOD 125 9 0 01 Nov 2019
Measurement Dependence Inducing Latent Causal ModelsConference on Uncertainty in Artificial Intelligence (UAI), 2019 Alex Markham Moritz Grosse-Wentrup CML 161 17 0 19 Oct 2019
Identifying through Flows for Recovering Latent RepresentationsInternational Conference on Learning Representations (ICLR), 2019 Shen Li Bryan Hooi Gim Hee Lee DRL OOD 238 14 0 27 Sep 2019
Dual Adversarial Inference for Text-to-Image SynthesisIEEE International Conference on Computer Vision (ICCV), 2019 Qicheng Lao Mohammad Havaei Ahmad Pesaranghader Francis Dutil Lisa Di-Jorio T. Fevens GAN 122 39 0 14 Aug 2019
Counterfactual Reasoning for Fair Clinical Risk PredictionMachine Learning in Health Care (MLHC), 2019 Stephen Pfohl Tony Duan D. Ding N. Shah OOD CML 121 63 0 14 Jul 2019