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2206.02416
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Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Neural Information Processing Systems (NeurIPS), 2022
6 June 2022
Patrik Reizinger
Luigi Gresele
Jack Brady
Julius von Kügelgen
Dominik Zietlow
Bernhard Schölkopf
Georg Martius
Wieland Brendel
M. Besserve
DRL
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Papers citing
"Embrace the Gap: VAEs Perform Independent Mechanism Analysis"
18 / 18 papers shown
Mechanistic Independence: A Principle for Identifiable Disentangled Representations
Stefan Matthes
Zhiwei Han
Hao Shen
CML
112
0
0
26 Sep 2025
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
Patrik Reizinger
Randall Balestriero
David Klindt
Wieland Brendel
716
4
0
17 Apr 2025
Robustness of Nonlinear Representation Learning
International Conference on Machine Learning (ICML), 2025
Simon Buchholz
Bernhard Schölkopf
OOD
901
10
0
19 Mar 2025
Unpicking Data at the Seams: Understanding Disentanglement in VAEs
Carl Allen
CML
CoGe
588
0
0
29 Oct 2024
Analyzing Generative Models by Manifold Entropic Metrics
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Daniel Galperin
Ullrich Köthe
DRL
481
1
0
25 Oct 2024
Causal Representation Learning in Temporal Data via Single-Parent Decoding
Philippe Brouillard
Sébastien Lachapelle
Julia Kaltenborn
Yaniv Gurwicz
Dhanya Sridhar
Alexandre Drouin
Peer Nowack
Jakob Runge
David Rolnick
CML
261
9
0
09 Oct 2024
Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
Xinzhe Luo
Xin Wang
Linda Shapiro
Chun Yuan
Jianfeng Feng
Xiahai Zhuang
400
0
0
04 Jan 2024
Independent Mechanism Analysis and the Manifold Hypothesis
Shubhangi Ghosh
Luigi Gresele
Julius von Kügelgen
M. Besserve
Bernhard Schölkopf
CML
381
5
0
20 Dec 2023
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
Aneesh Komanduri
Xintao Wu
Yongkai Wu
Feng Chen
CML
OOD
492
23
0
17 Oct 2023
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Klaus-Rudolf Kladny
Julius von Kügelgen
Bernhard Schölkopf
Michael Muehlebach
BDL
546
9
0
11 Oct 2023
Compositional Generalization from First Principles
Neural Information Processing Systems (NeurIPS), 2023
Thaddäus Wiedemer
Prasanna Mayilvahanan
Matthias Bethge
Wieland Brendel
OCL
240
63
0
10 Jul 2023
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Neural Information Processing Systems (NeurIPS), 2023
Sébastien Lachapelle
Divyat Mahajan
Ioannis Mitliagkas
Damien Scieur
332
49
0
05 Jul 2023
High Fidelity Image Counterfactuals with Probabilistic Causal Models
International Conference on Machine Learning (ICML), 2023
Fabio De Sousa Ribeiro
Tian Xia
M. Monteiro
Nick Pawlowski
Ben Glocker
DiffM
294
68
0
27 Jun 2023
BISCUIT: Causal Representation Learning from Binary Interactions
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Phillip Lippe
Sara Magliacane
Sindy Löwe
Yuki M. Asano
Taco S. Cohen
E. Gavves
CML
384
38
0
16 Jun 2023
Provably Learning Object-Centric Representations
International Conference on Machine Learning (ICML), 2023
Jack Brady
Roland S. Zimmermann
Yash Sharma
Bernhard Schölkopf
Julius von Kügelgen
Wieland Brendel
OCL
313
53
0
23 May 2023
Function Classes for Identifiable Nonlinear Independent Component Analysis
Neural Information Processing Systems (NeurIPS), 2022
Simon Buchholz
M. Besserve
Bernhard Schölkopf
232
50
0
12 Aug 2022
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
International Conference on Learning Representations (ICLR), 2022
Phillip Lippe
Sara Magliacane
Sindy Löwe
Yuki M. Asano
Taco S. Cohen
E. Gavves
CML
313
44
0
13 Jun 2022
Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders
Journal of machine learning research (JMLR), 2021
Lisa Bonheme
M. Grzes
DRL
336
8
0
26 Sep 2021
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