Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning

29 March 2023

Papers citing "Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning"

9 / 9 papers shown

Title
Analyzing Generative Models by Manifold Entropic Metrics Daniel Galperin Ullrich Köthe DRL 23 0 0 25 Oct 2024
Unsupervised Representation Learning from Sparse Transformation Analysis Yue Song Thomas Anderson Keller Yisong Yue Pietro Perona Max Welling DRL 29 0 0 07 Oct 2024
A Review of Change of Variable Formulas for Generative Modeling Ullrich Kothe 21 6 0 04 Aug 2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions Julius von Kügelgen M. Besserve Wendong Liang Luigi Gresele Armin Kekić Elias Bareinboim David M. Blei Bernhard Schölkopf CML 18 56 0 01 Jun 2023
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning Mhairi Dunion Trevor A. McInroe K. Luck Josiah P. Hanna Stefano V. Albrecht OOD DRL 22 17 0 23 May 2023
Identifiability of latent-variable and structural-equation models: from linear to nonlinear Aapo Hyvarinen Ilyes Khemakhem R. Monti CML 30 41 0 06 Feb 2023
Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann Yash Sharma Steffen Schneider Matthias Bethge Wieland Brendel SSL 238 207 0 17 Feb 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 144 660 0 28 Dec 2020
Multi-task self-supervised learning for Robust Speech Recognition Mirco Ravanelli Jianyuan Zhong Santiago Pascual P. Swietojanski João Monteiro J. Trmal Yoshua Bengio SSL 189 288 0 25 Jan 2020