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Disentangling the independently controllable factors of variation by
  interacting with the world

Disentangling the independently controllable factors of variation by interacting with the world

26 February 2018
Valentin Thomas
Emmanuel Bengio
W. Fedus
Jules Pondard
Philippe Beaudoin
Hugo Larochelle
Joelle Pineau
Doina Precup
Yoshua Bengio
    DRLCoGeCML
ArXiv (abs)PDFHTML

Papers citing "Disentangling the independently controllable factors of variation by interacting with the world"

42 / 42 papers shown
From Pixels to Factors: Learning Independently Controllable State Variables for Reinforcement Learning
From Pixels to Factors: Learning Independently Controllable State Variables for Reinforcement Learning
Rafael Rodríguez-Sánchez
Cameron Allen
George Konidaris
OffRL
184
2
0
02 Oct 2025
Interpretable Machine Learning in Physics: A Review
Interpretable Machine Learning in Physics: A Review
Sebastian Johann Wetzel
Seungwoong Ha
Raban Iten
Miriam Klopotek
Ziming Liu
AI4CE
395
12
0
30 Mar 2025
Disentangled Representations for Causal Cognition
Disentangled Representations for Causal Cognition
Filippo Torresan
Manuel Baltieri
CML
264
4
0
30 Jun 2024
Memory Mosaics
Memory MosaicsInternational Conference on Learning Representations (ICLR), 2024
Jianyu Zhang
Niklas Nolte
Ranajoy Sadhukhan
Beidi Chen
Léon Bottou
VLM
396
8
0
10 May 2024
Algebras of actions in an agent's representations of the world
Algebras of actions in an agent's representations of the worldArtificial Intelligence (AIJ), 2023
Alexander Dean
Eduardo Alonso
Esther Mondragón
291
0
0
02 Oct 2023
Reinforcement Learning with Exogenous States and Rewards
Reinforcement Learning with Exogenous States and Rewards
George Trimponias
Thomas G. Dietterich
OffRL
201
2
0
22 Mar 2023
Gromov-Wasserstein Autoencoders
Gromov-Wasserstein AutoencodersInternational Conference on Learning Representations (ICLR), 2022
Nao Nakagawa
Ren Togo
Takahiro Ogawa
Miki Haseyama
GANDRL
246
16
0
15 Sep 2022
elBERto: Self-supervised Commonsense Learning for Question Answering
elBERto: Self-supervised Commonsense Learning for Question AnsweringKnowledge-Based Systems (KBS), 2022
Xunlin Zhan
Yuan Li
Xiao Dong
Xiaodan Liang
Zhiting Hu
Lawrence Carin
SSLRALMLRM
184
9
0
17 Mar 2022
Comparing Reconstruction- and Contrastive-based Models for Visual Task
  Planning
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Constantinos Chamzas
M. Lippi
Michael C. Welle
Anastasia Varava
Lydia E. Kavraki
Danica Kragic
SSLDRLOffRL
189
5
0
14 Sep 2021
Self-supervised Reinforcement Learning with Independently Controllable
  Subgoals
Self-supervised Reinforcement Learning with Independently Controllable SubgoalsConference on Robot Learning (CoRL), 2021
Antonios Tragoudaras
Georg Martius
Fanny Yang
SSL
229
18
0
09 Sep 2021
Desiderata for Representation Learning: A Causal Perspective
Desiderata for Representation Learning: A Causal Perspective
Yixin Wang
Sai Li
CML
237
89
0
08 Sep 2021
Discovering Generalizable Skills via Automated Generation of Diverse
  Tasks
Discovering Generalizable Skills via Automated Generation of Diverse Tasks
Kuan Fang
Yuke Zhu
Silvio Savarese
Li Fei-Fei
180
8
0
26 Jun 2021
Measuring Disentanglement: A Review of Metrics
Measuring Disentanglement: A Review of MetricsIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020
M. Carbonneau
Julian Zaïdi
Jonathan Boilard
G. Gagnon
CoGeDRL
276
101
0
16 Dec 2020
Self-supervised Visual Reinforcement Learning with Object-centric
  Representations
Self-supervised Visual Reinforcement Learning with Object-centric RepresentationsInternational Conference on Learning Representations (ICLR), 2020
Antonios Tragoudaras
Maximilian Seitzer
Georg Martius
SSLOCL
214
47
0
29 Nov 2020
Affordance as general value function: A computational model
Affordance as general value function: A computational modelAdaptive Behavior (AB), 2020
D. Graves
Johannes Günther
Jun Luo
AI4CE
305
6
0
27 Oct 2020
A Sober Look at the Unsupervised Learning of Disentangled
  Representations and their Evaluation
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
183
77
0
27 Oct 2020
A Disentangled Adversarial Neural Topic Model for Separating Opinions
  from Plots in User Reviews
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews
Gabriele Pergola
Lin Gui
Yulan He
AAMLBDLDRL
227
21
0
22 Oct 2020
RODE: Learning Roles to Decompose Multi-Agent Tasks
RODE: Learning Roles to Decompose Multi-Agent TasksInternational Conference on Learning Representations (ICLR), 2020
Tonghan Wang
Tarun Gupta
Anuj Mahajan
Bei Peng
Shimon Whiteson
Chongjie Zhang
OffRL
301
258
0
04 Oct 2020
Revisiting Factorizing Aggregated Posterior in Learning Disentangled
  Representations
Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations
Ze Cheng
Juncheng Li
Chenxu Wang
Jixuan Gu
Hao Xu
Xinjian Li
Florian Metze
OOD
129
2
0
12 Sep 2020
Training Interpretable Convolutional Neural Networks by Differentiating
  Class-specific Filters
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific FiltersEuropean Conference on Computer Vision (ECCV), 2020
Haoyun Liang
Zhihao Ouyang
Yuyuan Zeng
Hang Su
Zihao He
Shutao Xia
Jun Zhu
Bo Zhang
289
50
0
16 Jul 2020
Model-based Reinforcement Learning: A Survey
Model-based Reinforcement Learning: A Survey
Thomas M. Moerland
Joost Broekens
Aske Plaat
Catholijn M. Jonker
OffRL
487
63
0
30 Jun 2020
From proprioception to long-horizon planning in novel environments: A
  hierarchical RL model
From proprioception to long-horizon planning in novel environments: A hierarchical RL model
Nishad Gothoskar
Miguel Lázaro-Gredilla
Dileep George
109
0
0
11 Jun 2020
Stable Prediction via Leveraging Seed Variable
Stable Prediction via Leveraging Seed Variable
Kun Kuang
Yangqiu Song
Peng Cui
Yue Liu
Jianrong Tao
Yueting Zhuang
Leilei Gan
OODCML
148
6
0
09 Jun 2020
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning
  Network by Incorporating Bayesian Inference
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian InferenceIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2020
Masashi Okada
Norio Kosaka
T. Taniguchi
188
43
0
01 Mar 2020
Resolving Spurious Correlations in Causal Models of Environments via
  Interventions
Resolving Spurious Correlations in Causal Models of Environments via Interventions
S. Volodin
Nevan Wichers
Jeremy Nixon
CML
221
18
0
12 Feb 2020
Weakly-Supervised Disentanglement Without Compromises
Weakly-Supervised Disentanglement Without CompromisesInternational Conference on Machine Learning (ICML), 2020
Francesco Locatello
Ben Poole
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
Michael Tschannen
CoGeOODDRL
665
347
0
07 Feb 2020
Operationally meaningful representations of physical systems in neural
  networks
Operationally meaningful representations of physical systems in neural networks
Hendrik Poulsen Nautrup
Tony Metger
Raban Iten
Sofiene Jerbi
Lea M. Trenkwalder
H. Wilming
Hans J. Briegel
R. Renner
AI4CENAI
152
30
0
02 Jan 2020
Deep Representation Learning in Speech Processing: Challenges, Recent
  Advances, and Future Trends
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
S. Latif
R. Rana
Sara Khalifa
Raja Jurdak
Junaid Qadir
Björn W. Schuller
AI4TS
346
88
0
02 Jan 2020
Contrastive Learning of Structured World Models
Contrastive Learning of Structured World ModelsInternational Conference on Learning Representations (ICLR), 2019
Thomas Kipf
Elise van der Pol
Max Welling
OCLDRL
408
305
0
27 Nov 2019
Learning First-Order Symbolic Representations for Planning from the
  Structure of the State Space
Learning First-Order Symbolic Representations for Planning from the Structure of the State SpaceEuropean Conference on Artificial Intelligence (ECAI), 2019
Blai Bonet
Hector Geffner
NAI
233
59
0
12 Sep 2019
Dynamics-Aware Unsupervised Discovery of Skills
Dynamics-Aware Unsupervised Discovery of SkillsInternational Conference on Learning Representations (ICLR), 2019
Archit Sharma
S. Gu
Sergey Levine
Vikash Kumar
Karol Hausman
349
451
0
02 Jul 2019
On the Fairness of Disentangled Representations
On the Fairness of Disentangled RepresentationsNeural Information Processing Systems (NeurIPS), 2019
Francesco Locatello
G. Abbati
Tom Rainforth
Stefan Bauer
Bernhard Schölkopf
Olivier Bachem
FaMLDRL
179
239
0
31 May 2019
Locality-Promoting Representation Learning
Locality-Promoting Representation LearningInternational Conference on Pattern Recognition (ICPR), 2019
Johannes Schneider
135
2
0
25 May 2019
The Journey is the Reward: Unsupervised Learning of Influential
  Trajectories
The Journey is the Reward: Unsupervised Learning of Influential Trajectories
Jonathan Binas
Sherjil Ozair
Yoshua Bengio
SSL
63
4
0
22 May 2019
Disentangling Factors of Variation Using Few Labels
Disentangling Factors of Variation Using Few Labels
Francesco Locatello
Michael Tschannen
Stefan Bauer
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
DRLCMLCoGe
202
126
0
03 May 2019
ToyArchitecture: Unsupervised Learning of Interpretable Models of the
  World
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Jaroslav Vítků
Petr Dluhos
Joseph Davidson
Matej Nikl
Simon Andersson
...
Martin Stránský
M. Hyben
Martin Poliak
Jan Feyereisl
Marek Rosa
AI4CESSL
256
0
0
20 Mar 2019
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps
  for Time Series Prediction
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
Bryan Lim
S. Zohren
Stephen J. Roberts
BDLAI4TS
192
47
0
23 Jan 2019
Challenging Common Assumptions in the Unsupervised Learning of
  Disentangled Representations
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello
Stefan Bauer
Mario Lucic
Gunnar Rätsch
Sylvain Gelly
Bernhard Schölkopf
Olivier Bachem
OOD
574
1,613
0
29 Nov 2018
Towards Governing Agent's Efficacy: Action-Conditional $β$-VAE for
  Deep Transparent Reinforcement Learning
Towards Governing Agent's Efficacy: Action-Conditional βββ-VAE for Deep Transparent Reinforcement Learning
John Yang
Gyujeong Lee
Minsung Hyun
Simyung Chang
Nojun Kwak
138
3
0
11 Nov 2018
Curiosity Driven Exploration of Learned Disentangled Goal Spaces
Curiosity Driven Exploration of Learned Disentangled Goal SpacesConference on Robot Learning (CoRL), 2018
A. Laversanne-Finot
Alexandre Péré
Pierre-Yves Oudeyer
DRL
207
89
0
04 Jul 2018
Disentangling Controllable and Uncontrollable Factors of Variation by
  Interacting with the World
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World
Yoshihide Sawada
DRL
210
10
0
19 Apr 2018
Music Style Transfer: A Position Paper
Music Style Transfer: A Position Paper
Shuqi Dai
Zheng Zhang
Gus G. Xia
427
55
0
19 Mar 2018
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