Multifactor Sequential Disentanglement via Structured Koopman Autoencoders

30 March 2023

ArXiv (abs)PDF HTML Github (18★)

Papers citing "Multifactor Sequential Disentanglement via Structured Koopman Autoencoders"

38 / 38 papers shown

Title
An Operator Theoretic View on Pruning Deep Neural Networks William T. Redman M. Fonoberova Ryan Mohr Yannis G. Kevrekidis Igor Mezić 82 17 0 28 Oct 2021
Contrastively Disentangled Sequential Variational Autoencoder M. Kiener Weiran Wang Michael Gerndt CoGe DRL 96 42 0 22 Oct 2021
Self-Supervised Decomposition, Disentanglement and Prediction of Video Sequences while Interpreting Dynamics: A Koopman Perspective Armand Comas Massague S. Ghimire Haolin Li Octavia Camps Mario Sznaier 91 2 0 01 Oct 2021
DisUnknown: Distilling Unknown Factors for Disentanglement Learning Sitao Xiang Yuming Gu Pengda Xiang Menglei Chai Hao Li Yajie Zhao Mingming He CoGe 138 4 0 16 Sep 2021
Modern Koopman Theory for Dynamical Systems Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 118 420 0 24 Feb 2021
An Operator Theoretic Approach for Analyzing Sequence Neural Networks Ilana D Naiman Omri Azencot 90 11 0 15 Feb 2021
Disentangled Recurrent Wasserstein Autoencoder Jun Han Martin Renqiang Min Ligong Han Erran L. Li Xuan Zhang CoGe SyDa DRL 65 33 0 19 Jan 2021
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics Tomoharu Iwata Yoshinobu Kawahara 53 12 0 11 Dec 2020
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components Junwen Bai Weiran Wang Yingbo Zhou Caiming Xiong SSL AI4TS 69 12 0 07 Oct 2020
Dynamical Variational Autoencoders: A Comprehensive Review Laurent Girin Simon Leglaive Xiaoyu Bie Julien Diard Thomas Hueber Xavier Alameda-Pineda BDL 103 219 0 28 Aug 2020
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation Yizhe Zhu Martin Renqiang Min Asim Kadav H. Graf CoGe DRL 61 96 0 23 May 2020
Forecasting Sequential Data using Consistent Koopman Autoencoders Omri Azencot N. Benjamin Erichson Vanessa Lin Michael W. Mahoney AI4TS AI4CE 185 152 0 04 Mar 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 553 42,639 0 03 Dec 2019
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation Yuheng Li Krishna Kumar Singh Utkarsh Ojha Yong Jae Lee CoGe 41 77 0 26 Nov 2019
Learning Compositional Koopman Operators for Model-Based Control Yunzhu Li Hao He Jiajun Wu Dina Katabi Antonio Torralba 103 121 0 18 Oct 2019
Disentangled State Space Representations Ðorðe Miladinovic Muhammad Waleed Gondal Bernhard Schölkopf J. M. Buhmann Stefan Bauer DRL 51 30 0 07 Jun 2019
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis David G. Clark J. Livezey K. Bouchard AI4TS 83 24 0 23 May 2019
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 141 1,473 0 29 Nov 2018
Deep Dynamical Modeling and Control of Unsteady Fluid Flows Jeremy Morton F. Witherden A. Jameson Mykel J. Kochenderfer AI4CE 70 165 0 18 May 2018
Disentangled Sequential Autoencoder Yingzhen Li Stephan Mandt CoGe 73 272 0 08 Mar 2018
Disentangling by Factorising Hyunjik Kim A. Mnih CoGe OOD 70 1,356 0 16 Feb 2018
Deep learning for universal linear embeddings of nonlinear dynamics Bethany Lusch J. Nathan Kutz Steven L. Brunton 81 1,260 0 27 Dec 2017
Linearly-Recurrent Autoencoder Networks for Learning Dynamics Samuel E. Otto C. Rowley AI4CE 60 328 0 04 Dec 2017
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations Abhishek Kumar P. Sattigeri Avinash Balakrishnan BDL DRL 93 523 0 02 Nov 2017
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning Marco Fraccaro Simon Kamronn Ulrich Paquet Ole Winther BDL 90 284 0 16 Oct 2017
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition Naoya Takeishi Yoshinobu Kawahara Takehisa Yairi 51 374 0 12 Oct 2017
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data Wei-Ning Hsu Yu Zhang James R. Glass BDL SSL 84 354 0 22 Sep 2017
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems Enoch Yeung Soumya Kundu Nathan Oken Hodas AI4CE 79 387 0 22 Aug 2017
MoCoGAN: Decomposing Motion and Content for Video Generation Sergey Tulyakov Ming-Yuan Liu Xiaodong Yang Jan Kautz GAN 147 1,150 0 17 Jul 2017
Decomposing Motion and Content for Natural Video Sequence Prediction Ruben Villegas Jimei Yang Seunghoon Hong Xunyu Lin Honglak Lee 90 598 0 25 Jun 2017
Unsupervised Learning of Disentangled Representations from Video Emily L. Denton Vighnesh Birodkar DRL CoGe OOD 88 553 0 31 May 2017
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations Diane Bouchacourt Ryota Tomioka Sebastian Nowozin BDL OOD DRL 66 314 0 24 May 2017
Learning Hierarchical Features from Generative Models Shengjia Zhao Jiaming Song Stefano Ermon BDL GAN OOD DRL 78 74 0 27 Feb 2017
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel GAN 161 4,238 0 12 Jun 2016
Deep Convolutional Inverse Graphics Network Tejas D. Kulkarni William F. Whitney Pushmeet Kohli J. Tenenbaum DRL BDL 103 930 0 11 Mar 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,364 0 22 Dec 2014
Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio AIMat 578 27,338 0 01 Sep 2014
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 286 12,460 0 24 Jun 2012