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Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images.
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Aapo Hyvärinen and Hiroshi Morioka · 2016
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Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
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Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
Peter Sorrenson, Carsten Rother, and Ullrich Köthe · 2020
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Causal imitation learning with unobserved confounders
Junzhe Zhang, Daniel Kumor, and Elias Bareinboim · 2020
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Mutual Information Neural Estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
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Independent mechanism analysis, a new concept?
Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, and Michel Besserve · 2021
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Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
David Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dylan Paiton · 2021
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Extending the spectral decomposition of granger causality to include instantaneous influences: application to the control mechanisms of heart rate variability
D Nuzzi, S Stramaglia, M Javorka, Daniele Marinazzo, A Porta, and Luca Faes · 2021
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Suggestive Contour Gallery
Szymon Rusinkiewicz, Doug DeCarlo, Adam Finkelstein, and Anothony Santella · 2021
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Toward causal representation learning
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Causal influence detection for improving efficiency in reinforcement learning
Maximilian Seitzer, Bernhard Schölkopf, and Georg Martius · 2021
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On Disentangled Representations Learned from Correlated Data
Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer · 2021
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Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello · 2021
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CausalVAE: disentangled representation learning via neural structural causal models
Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang · 2021
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Contrastive Learning Inverts the Data Generating Process
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel · 2021
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Weakly Supervised Representation Learning with Sparse Perturbations
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Weakly supervised causal representation learning
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Conditional Object-Centric Learning from Video
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Masked Gradient-Based Causal Structure Learning
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Embrace the Gap: VAEs Perform Independent Mechanism Analysis
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