A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

16 October 2017

Papers citing "A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning"

50 / 61 papers shown

Title
Towards Latency-Aware 3D Streaming Perception for Autonomous Driving Jiaqi Peng Tai Wang Jiangmiao Pang Yuan Shen 47 0 0 27 Apr 2025
Continuous Locomotive Crowd Behavior Generation Inhwan Bae Junoh Lee Hae-Gon Jeon 31 0 0 07 Apr 2025
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability Carlos E. Luis A. Bottero Julia Vinogradska Felix Berkenkamp Jan Peters 78 1 0 20 Feb 2025
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations Julius Aka Johannes Brunnemann Jörg Eiden Arne Speerforck Lars Mikelsons 31 0 0 14 Oct 2024
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series Byoungwoo Park Hyungi Lee Juho Lee AI4TS 46 0 0 08 Oct 2024
Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction Yuan Xue Nan Du A. Mottram Martin G. Seneviratne Andrew M. Dai AI4TS 55 0 0 28 Jul 2024
Exploiting the capacity of deep networks only at training stage for nonlinear black-box system identification V. M. Eivaghi M. A. Shoorehdeli 34 0 0 26 Dec 2023
DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup Anubhab Ghosh Antoine Honoré S. Chatterjee 26 20 0 04 Jun 2023
Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts Jan Achterhold Philip Tobuschat Hao Ma Dieter Buechler Michael Muehlebach Joerg Stueckler 14 6 0 24 May 2023
Multifactor Sequential Disentanglement via Structured Koopman Autoencoders Nimrod Berman Ilana D Naiman Omri Azencot CoGe 32 22 0 30 Mar 2023
Criteria for Classifying Forecasting Methods Tim Januschowski Jan Gasthaus Bernie Wang David Salinas Valentin Flunkert Michael Bohlke-Schneider Laurent Callot AI4TS 21 173 0 07 Dec 2022
Temporally Disentangled Representation Learning Weiran Yao Guangyi Chen Kun Zhang CML BDL OOD 32 48 0 24 Oct 2022
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning P. Becker Gerhard Neumann 30 9 0 17 Oct 2022
Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems Wei Liu Zhilu Lai Kiran Bacsa Eleni Chatzi 38 16 0 09 Oct 2022
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations Paidamoyo Chapfuwa Sherri Rose Lawrence Carin Edward Meeds Ricardo Henao CML 22 1 0 25 Feb 2022
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations Simon Bing Andrea Dittadi Stefan Bauer Patrick Schwab SyDa 25 17 0 20 Jan 2022
Estimating the Value-at-Risk by Temporal VAE Robert Sicks S. Grimm R. Korn Ivo Richert 15 6 0 03 Dec 2021
Hamiltonian latent operators for content and motion disentanglement in image sequences Asif Khan Amos Storkey 29 2 0 02 Dec 2021
Survey of Deep Learning Methods for Inverse Problems S. Kamyab Zihreh Azimifar Rasool Sabzi Paul Fieguth 21 3 0 07 Nov 2021
Deep Explicit Duration Switching Models for Time Series Abdul Fatir Ansari Konstantinos Benidis Richard Kurle Ali Caner Turkmen Harold Soh Alex Smola Yuyang Wang Tim Januschowski BDL 16 19 0 26 Oct 2021
Contrastively Disentangled Sequential Variational Autoencoder M. Kiener Weiran Wang Michael Gerndt CoGe DRL 27 40 0 22 Oct 2021
Unsupervised Learned Kalman Filtering Guy Revach Nir Shlezinger Timur Locher Xiaoyong Ni Ruud J. G. van Sloun Yonina C. Eldar SSL 31 31 0 18 Oct 2021
Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty Wei Liu Zhilu Lai Kiran Bacsa Eleni Chatzi PINN BDL AI4CE 20 33 0 16 Oct 2021
Non-local Graph Convolutional Network for joint Activity Recognition and Motion Prediction Dianhao Zhang Ngo Anh Vien Mien Van Seán F. McLoone 3DH HAI 29 6 0 03 Aug 2021
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics Guy Revach Nir Shlezinger Xiaoyong Ni Adrià López Escoriza Ruud J. G. van Sloun Yonina C. Eldar 31 264 0 21 Jul 2021
ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models Tijin Yan Hongwei Zhang Tong Zhou Yufeng Zhan Yuanqing Xia DiffM AI4TS 36 38 0 18 Jun 2021
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering Cheng Feng Pengwei Tian AI4TS 19 75 0 15 Jun 2021
A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling Xiaoyu Bie Laurent Girin Simon Leglaive Thomas Hueber Xavier Alameda-Pineda 26 12 0 11 Jun 2021
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting Soumyasundar Pal Liheng Ma Yingxue Zhang Mark J. Coates BDL AI4TS 33 22 0 10 Jun 2021
Data-driven Analysis for Understanding Team Sports Behaviors Keisuke Fujii AI4TS AI4CE 37 27 0 15 Feb 2021
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs Cheng Wang Carolin (Haas) Lawrence Mathias Niepert UQCV 29 10 0 24 Nov 2020
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning Jung-Su Ha Young-Jin Park Hyeok-Joo Chae Soon-Seo Park Han-Lim Choi 30 3 0 16 Nov 2020
Variational Dynamic Mixtures Chen Qiu Stephan Mandt Maja R. Rudolph BDL AI4TS 16 2 0 20 Oct 2020
S2RMs: Spatially Structured Recurrent Modules Nasim Rahaman Anirudh Goyal Muhammad Waleed Gondal M. Wuthrich Stefan Bauer Yash Sharma Yoshua Bengio Bernhard Schölkopf 21 14 0 13 Jul 2020
Learning Continuous-Time Dynamics by Stochastic Differential Networks Yingru Liu Yucheng Xing Xuewen Yang Xin Wang Jing Shi Di Jin Zhaoyue Chen BDL 26 6 0 11 Jun 2020
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey Konstantinos Benidis Syama Sundar Rangapuram Valentin Flunkert Bernie Wang Danielle C. Maddix ... David Salinas Lorenzo Stella François-Xavier Aubet Laurent Callot Tim Januschowski AI4TS 25 176 0 21 Apr 2020
PatchVAE: Learning Local Latent Codes for Recognition Kamal Gupta Saurabh Singh Abhinav Shrivastava SSL DRL 14 20 0 07 Apr 2020
Learning to Fly via Deep Model-Based Reinforcement Learning Philip Becker-Ehmck Maximilian Karl Jan Peters Patrick van der Smagt SSL 35 37 0 19 Mar 2020
q-VAE for Disentangled Representation Learning and Latent Dynamical Systems Taisuke Kobayashis BDL DRL 19 17 0 04 Mar 2020
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction Vincent Le Guen Nicolas Thome AI4CE PINN 91 289 0 03 Mar 2020
Stochastic Latent Residual Video Prediction Jean-Yves Franceschi E. Delasalles Mickaël Chen Sylvain Lamprier Patrick Gallinari VGen 26 159 0 21 Feb 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 184 313 0 07 Feb 2020
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems Zhe Dong Bryan Seybold Kevin Patrick Murphy Hung Bui BDL 32 30 0 21 Oct 2019
Recurrent Attentive Neural Process for Sequential Data Shenghao Qin Jiacheng Zhu Jimmy Qin Wenshuo Wang Ding Zhao BDL AI4TS 27 38 0 17 Oct 2019
A General Framework for Uncertainty Estimation in Deep Learning Antonio Loquercio Mattia Segu Davide Scaramuzza UQCV BDL OOD 31 289 0 16 Jul 2019
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model Alex X. Lee Anusha Nagabandi Pieter Abbeel Sergey Levine OffRL BDL 27 372 0 01 Jul 2019
Sequential Neural Processes Gautam Singh Jaesik Yoon Youngsung Son Sungjin Ahn BDL AI4TS 40 81 0 24 Jun 2019
Recurrent Neural Processes Timon Willi Jonathan Masci Jürgen Schmidhuber Christian Osendorfer BDL 18 18 0 13 Jun 2019
Deep Factors for Forecasting Bernie Wang Alex Smola Danielle C. Maddix Jan Gasthaus Dean Phillips Foster Tim Januschowski BDL 18 170 0 28 May 2019
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video Miguel Jaques Michael G. Burke Timothy M. Hospedales VGen PINN 21 45 0 27 May 2019