ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.07886
  4. Cited By
On Disentangled Representations Learned From Correlated Data

On Disentangled Representations Learned From Correlated Data

14 June 2020
Frederik Trauble
Elliot Creager
Niki Kilbertus
Francesco Locatello
Andrea Dittadi
Anirudh Goyal
Bernhard Schölkopf
Stefan Bauer
    OOD
    CML
ArXivPDFHTML

Papers citing "On Disentangled Representations Learned From Correlated Data"

26 / 26 papers shown
Title
Analyzing (In)Abilities of SAEs via Formal Languages
Analyzing (In)Abilities of SAEs via Formal Languages
Abhinav Menon
Manish Shrivastava
David M. Krueger
Ekdeep Singh Lubana
42
7
0
15 Oct 2024
When does compositional structure yield compositional generalization? A kernel theory
When does compositional structure yield compositional generalization? A kernel theory
Samuel Lippl
Kim Stachenfeld
NAI
CoGe
67
5
0
26 May 2024
Learned feature representations are biased by complexity, learning
  order, position, and more
Learned feature representations are biased by complexity, learning order, position, and more
Andrew Kyle Lampinen
Stephanie C. Y. Chan
Katherine Hermann
AI4CE
FaML
SSL
OOD
32
6
0
09 May 2024
Towards Controllable Time Series Generation
Towards Controllable Time Series Generation
Yifan Bao
Yihao Ang
Qiang Huang
Anthony K. H. Tung
Zhiyong Huang
DiffM
38
4
0
06 Mar 2024
Disentangling representations of retinal images with generative models
Disentangling representations of retinal images with generative models
Sarah Muller
Lisa M. Koch
Hendrik P. A. Lensch
Philipp Berens
MedIm
32
3
0
29 Feb 2024
Identifying Semantic Component for Robust Molecular Property Prediction
Identifying Semantic Component for Robust Molecular Property Prediction
Zijian Li
Zunhong Xu
Ruichu Cai
Zhenhui Yang
Yuguang Yan
Zhifeng Hao
Guan-Hong Chen
Kun Zhang
21
9
0
08 Nov 2023
Disentanglement Learning via Topology
Disentanglement Learning via Topology
Nikita Balabin
Daria Voronkova
I. Trofimov
Evgeny Burnaev
S. Barannikov
DRL
55
2
0
24 Aug 2023
On Counterfactual Data Augmentation Under Confounding
On Counterfactual Data Augmentation Under Confounding
Abbavaram Gowtham Reddy
Saketh Bachu
Saloni Dash
Charchit Sharma
Amit Sharma
V. Balasubramanian
CML
BDL
30
0
0
29 May 2023
Causal Triplet: An Open Challenge for Intervention-centric Causal
  Representation Learning
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Yuejiang Liu
Alexandre Alahi
Chris Russell
Max Horn
Dominik Zietlow
Bernhard Schölkopf
Francesco Locatello
CML
54
22
0
12 Jan 2023
Disentangled Representation Learning
Disentangled Representation Learning
Xin Eric Wang
Hong Chen
Siao Tang
Zihao Wu
Wenwu Zhu
DRL
29
77
0
21 Nov 2022
Exploiting Personalized Invariance for Better Out-of-distribution
  Generalization in Federated Learning
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning
Xueyang Tang
Song Guo
Jie M. Zhang
FedML
OODD
OOD
36
3
0
21 Nov 2022
Disentangling Content and Motion for Text-Based Neural Video
  Manipulation
Disentangling Content and Motion for Text-Based Neural Video Manipulation
Levent Karacan
Tolga Kerimouglu
.Ismail .Inan
Tolga Birdal
Erkut Erdem
Aykut Erdem
18
1
0
05 Nov 2022
Variance Covariance Regularization Enforces Pairwise Independence in
  Self-Supervised Representations
Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations
Grégoire Mialon
Randall Balestriero
Yann LeCun
24
9
0
29 Sep 2022
A Comprehensive Review of Trends, Applications and Challenges In
  Out-of-Distribution Detection
A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection
Navid Ghassemi
E. F. Ersi
AAML
OODD
17
4
0
26 Sep 2022
Equivariant Disentangled Transformation for Domain Generalization under
  Combination Shift
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift
Yivan Zhang
Jindong Wang
Xingxu Xie
Masashi Sugiyama
OOD
34
1
0
03 Aug 2022
Predicting is not Understanding: Recognizing and Addressing
  Underspecification in Machine Learning
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning
Damien Teney
Maxime Peyrard
Ehsan Abbasnejad
30
29
0
06 Jul 2022
When are Post-hoc Conceptual Explanations Identifiable?
When are Post-hoc Conceptual Explanations Identifiable?
Tobias Leemann
Michael Kirchhof
Yao Rong
Enkelejda Kasneci
Gjergji Kasneci
50
10
0
28 Jun 2022
Disentangling A Single MR Modality
Disentangling A Single MR Modality
Lianrui Zuo
Yihao Liu
Yuan Xue
Shuo Han
M. Bilgel
Susan M. Resnick
Jerry L. Prince
A. Carass
19
14
0
10 May 2022
CITRIS: Causal Identifiability from Temporal Intervened Sequences
CITRIS: Causal Identifiability from Temporal Intervened Sequences
Phillip Lippe
Sara Magliacane
Sindy Lowe
Yuki M. Asano
Taco S. Cohen
E. Gavves
CML
33
101
0
07 Feb 2022
On Causally Disentangled Representations
On Causally Disentangled Representations
Abbavaram Gowtham Reddy
Benin Godfrey L
V. Balasubramanian
OOD
CML
26
21
0
10 Dec 2021
Identifiable Deep Generative Models via Sparse Decoding
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran
Dhanya Sridhar
Yixin Wang
David M. Blei
BDL
23
44
0
20 Oct 2021
Be More Active! Understanding the Differences between Mean and Sampled
  Representations of Variational Autoencoders
Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders
Lisa Bonheme
M. Grzes
DRL
11
6
0
26 Sep 2021
Towards Out-Of-Distribution Generalization: A Survey
Towards Out-Of-Distribution Generalization: A Survey
Jiashuo Liu
Zheyan Shen
Yue He
Xingxuan Zhang
Renzhe Xu
Han Yu
Peng Cui
CML
OOD
29
515
0
31 Aug 2021
Weakly-Supervised Disentanglement Without Compromises
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello
Ben Poole
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
Michael Tschannen
CoGe
OOD
DRL
173
313
0
07 Feb 2020
Independent Subspace Analysis for Unsupervised Learning of Disentangled
  Representations
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Jan Stühmer
Richard E. Turner
Sebastian Nowozin
DRL
BDL
CoGe
109
25
0
05 Sep 2019
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
233
673
0
17 Feb 2018
1