Variational Autoencoders and Nonlinear ICA: A Unifying Framework

10 July 2019

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

32 / 132 papers shown

Title
Learning Disentangled Representations in the Imaging Domain Xiao Liu Pedro Sanchez Spyridon Thermos Alison Q. OÑeil Sotirios A. Tsaftaris OOD DRL 34 71 0 26 Aug 2021
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects Yao Zhang Jeroen Berrevoets M. Schaar CML 41 6 0 06 Aug 2021
Improving Music Performance Assessment with Contrastive Learning Pavan Seshadri Alexander Lerch 27 8 0 03 Aug 2021
Learning latent causal graphs via mixture oracles Bohdan Kivva Goutham Rajendran Pradeep Ravikumar Bryon Aragam CML 38 47 0 29 Jun 2021
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments Yining Chen Elan Rosenfeld Mark Sellke Tengyu Ma Andrej Risteski OOD 36 33 0 18 Jun 2021
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization Kartik Ahuja Ethan Caballero Dinghuai Zhang Jean-Christophe Gagnon-Audet Yoshua Bengio Ioannis Mitliagkas Irina Rish OOD 15 253 0 11 Jun 2021
Independent mechanism analysis, a new concept? Luigi Gresele Julius von Kügelgen Vincent Stimper Bernhard Schölkopf M. Besserve CML 23 100 0 09 Jun 2021
Causal Hidden Markov Model for Time Series Disease Forecasting Jing Li Botong Wu Xinwei Sun Yizhou Wang CML OOD 21 25 0 30 Mar 2021
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations Pau Rodríguez López Massimo Caccia Alexandre Lacoste L. Zamparo I. Laradji Laurent Charlin David Vazquez AAML 39 55 0 18 Mar 2021
Information Maximization Clustering via Multi-View Self-Labelling Foivos Ntelemis Yaochu Jin S. Thomas SSL 21 25 0 12 Mar 2021
Towards Building A Group-based Unsupervised Representation Disentanglement Framework Tao Yang Xuanchi Ren Yuwang Wang W. Zeng Nanning Zheng CoGe DRL 26 27 0 20 Feb 2021
Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann Yash Sharma Steffen Schneider Matthias Bethge Wieland Brendel SSL 238 211 0 17 Feb 2021
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems Kursat Rasim Mestav Xinyi Wang Lang Tong AI4TS 20 27 0 09 Dec 2020
Causal Autoregressive Flows Ilyes Khemakhem R. Monti R. Leech Aapo Hyvarinen CML OOD AI4CE 27 108 0 04 Nov 2020
Latent Causal Invariant Model Xinwei Sun Botong Wu Xiangyu Zheng Chang-Shu Liu Wei Chen Tao Qin Tie-Yan Liu OOD CML BDL 31 14 0 04 Nov 2020
Learning Causal Semantic Representation for Out-of-Distribution Prediction Chang-Shu Liu Xinwei Sun Jindong Wang Haoyue Tang Tao Li Tao Qin Wei Chen Tie-Yan Liu CML OODD OOD 35 104 0 03 Nov 2020
On the Transfer of Disentangled Representations in Realistic Settings Andrea Dittadi Frederik Trauble Francesco Locatello M. Wuthrich Vaibhav Agrawal Ole Winther Stefan Bauer Bernhard Schölkopf OOD 35 80 0 27 Oct 2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 17 66 0 27 Oct 2020
Product Manifold Learning Sharon Zhang Amit Moscovich A. Singer 44 14 0 19 Oct 2020
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations Markos Georgopoulos Grigorios G. Chrysos Maja Pantic Yannis Panagakis GAN DRL 24 17 0 09 Sep 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 48 132 0 21 Jul 2020
Time Series Source Separation with Slow Flows Edouard Pineau S. Razakarivony Thomas Bonald BDL AI4TS 47 2 0 20 Jul 2020
Identifying Latent Stochastic Differential Equations Ali Hasan João M. Pereira Sina Farsiu Vahid Tarokh DiffM 27 18 0 12 Jul 2020
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process H. Morioka Hermanni Hälvä Aapo Hyvarinen 25 22 0 19 Jun 2020
On Disentangled Representations Learned From Correlated Data Frederik Trauble Elliot Creager Niki Kilbertus Francesco Locatello Andrea Dittadi Anirudh Goyal Bernhard Schölkopf Stefan Bauer OOD CML 29 115 0 14 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 27 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 41 44 0 18 Apr 2020
A theory of independent mechanisms for extrapolation in generative models M. Besserve Rémy Sun Dominik Janzing Bernhard Schölkopf 28 25 0 01 Apr 2020
Few-shot Domain Adaptation by Causal Mechanism Transfer Takeshi Teshima Issei Sato Masashi Sugiyama OOD CML TTA 33 86 0 10 Feb 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 184 314 0 07 Feb 2020
Measurement Dependence Inducing Latent Causal Models Alex Markham Moritz Grosse-Wentrup CML 23 15 0 19 Oct 2019
DIVA: Domain Invariant Variational Autoencoders Maximilian Ilse Jakub M. Tomczak Christos Louizos Max Welling DRL OOD 35 198 0 24 May 2019