How to Train Your Differentiable Filter

28 December 2020

Papers citing "How to Train Your Differentiable Filter"

30 / 30 papers shown

Title
Dynamical Variational Autoencoders: A Comprehensive Review Laurent Girin Simon Leglaive Xiaoyu Bie Julien Diard Thomas Hueber Xavier Alameda-Pineda BDL 47 212 0 28 Aug 2020
Towards Differentiable Resampling Michael Zhu Kevin Patrick Murphy Rico Jonschkowski 36 27 0 24 Apr 2020
Learning to Control PDEs with Differentiable Physics Philipp Holl V. Koltun Nils Thuerey AI4CE PINN 58 187 0 21 Jan 2020
Particle Filter Recurrent Neural Networks Xiao Ma Peter Karkus David Hsu Wee Sun Lee 45 82 0 30 May 2019
Differentiable Algorithm Networks for Composable Robot Learning Peter Karkus Xiao Ma David Hsu L. Kaelbling Wee Sun Lee Tomas Lozano-Perez 35 71 0 28 May 2019
Differentiable MPC for End-to-end Planning and Control Brandon Amos I. D. Rodriguez Jacob Sacks Byron Boots J. Zico Kolter 41 366 0 31 Oct 2018
Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors Rico Jonschkowski Divyam Rastogi Oliver Brock 41 135 0 28 May 2018
Particle Filter Networks with Application to Visual Localization Peter Karkus David Hsu Wee Sun Lee 3DPC 41 117 0 23 May 2018
MPC-Inspired Neural Network Policies for Sequential Decision Making M. Pereira David D. Fan G. N. An Evangelos Theodorou BDL 37 38 0 15 Feb 2018
Learning to Search with MCTSnets A. Guez T. Weber Ioannis Antonoglou Karen Simonyan Oriol Vinyals Daan Wierstra Rémi Munos David Silver 54 85 0 13 Feb 2018
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning Gregory Farquhar Tim Rocktaschel Maximilian Igl Shimon Whiteson OffRL 48 71 0 31 Oct 2017
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning Marco Fraccaro Simon Kamronn Ulrich Paquet Ole Winther BDL 41 282 0 16 Oct 2017
Combining Learned and Analytical Models for Predicting Action Effects from Sensory Data Alina Kloss S. Schaal Jeannette Bohg 46 85 0 11 Oct 2017
Value Prediction Network Junhyuk Oh Satinder Singh Honglak Lee 60 332 0 11 Jul 2017
Path Integral Networks: End-to-End Differentiable Optimal Control Masashi Okada Luca Rigazio T. Aoshima PINN 46 56 0 29 Jun 2017
Variational Sequential Monte Carlo C. A. Naesseth Scott W. Linderman Rajesh Ranganath David M. Blei BDL 117 214 0 31 May 2017
Auto-Encoding Sequential Monte Carlo T. Le Maximilian Igl Tom Rainforth Tom Jin Frank Wood BDL DRL 164 151 0 29 May 2017
Filtering Variational Objectives Chris J. Maddison Dieterich Lawson George Tucker N. Heess Mohammad Norouzi A. Mnih Arnaud Doucet Yee Whye Teh FedML 98 210 0 25 May 2017
A probabilistic data-driven model for planar pushing Maria Bauzá Alberto Rodriguez 41 104 0 10 Apr 2017
QMDP-Net: Deep Learning for Planning under Partial Observability Peter Karkus David Hsu Wee Sun Lee PINN 84 156 0 20 Mar 2017
Structured Inference Networks for Nonlinear State Space Models Rahul G. Krishnan Uri Shalit David Sontag BDL 56 452 0 30 Sep 2016
Backprop KF: Learning Discriminative Deterministic State Estimators Tuomas Haarnoja Anurag Ajay Sergey Levine Pieter Abbeel 34 201 0 23 May 2016
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data Maximilian Karl Maximilian Sölch Justin Bayer Patrick van der Smagt BDL 26 373 0 20 May 2016
On the Effects of Measurement Uncertainty in Optimal Control of Contact Interactions Brahayam Pontón S. Schaal Ludovic Righetti 16 7 0 13 May 2016
More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing Kuan-Ting Yu Maria Bauzá Nima Fazeli Alberto Rodriguez 23 181 0 14 Apr 2016
Value Iteration Networks Aviv Tamar Yi Wu G. Thomas Sergey Levine Pieter Abbeel 52 650 0 09 Feb 2016
Black box variational inference for state space models Evan Archer Il Memming Park Lars Buesing John P. Cunningham Liam Paninski BDL 53 160 0 23 Nov 2015
Robust Gaussian Filtering using a Pseudo Measurement Manuel Wüthrich C. Cifuentes Sebastian Trimpe Franziska Meier Jeannette Bohg J. Issac S. Schaal 26 17 0 14 Sep 2015
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images Manuel Watter Jost Tobias Springenberg Joschka Boedecker Martin Riedmiller BDL 39 839 0 24 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 577 149,474 0 22 Dec 2014