Variational Marginal Particle Filters

30 September 2021

Papers citing "Variational Marginal Particle Filters"

40 / 40 papers shown

Title
Online Variational Filtering and Parameter Learning Andrew Campbell Yuyang Shi Tom Rainforth Arnaud Doucet BDL 55 21 0 26 Oct 2021
Learning Proposals for Probabilistic Programs with Inference Combinators Sam Stites Heiko Zimmermann Hao Wu Eli Sennesh Jan-Willem van de Meent NAI 28 14 0 01 Mar 2021
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport Adrien Corenflos James Thornton George Deligiannidis Arnaud Doucet OT 57 68 0 15 Feb 2021
Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization Nicola Branchini Victor Elvira 52 18 0 18 Nov 2020
Towards Differentiable Resampling Michael Zhu Kevin Patrick Murphy Rico Jonschkowski 41 27 0 24 Apr 2020
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations Xiao Ma Peter Karkus David Hsu W. Lee N. Ye OffRL 36 43 0 23 Feb 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 141 1,662 0 05 Dec 2019
Particle Smoothing Variational Objectives A. Moretti Zizhao Wang Luhuan Wu Iddo Drori I. Pe’er 51 10 0 20 Sep 2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation Justin Domke Daniel Sheldon 122 18 0 24 Jun 2019
Particle Filter Recurrent Neural Networks Xiao Ma Peter Karkus David Hsu Wee Sun Lee 50 82 0 30 May 2019
Copula-like Variational Inference Marcel Hirt P. Dellaportas Alain Durmus 24 5 0 15 Apr 2019
Elements of Sequential Monte Carlo C. A. Naesseth Fredrik Lindsten Thomas B. Schon 51 97 0 12 Mar 2019
An Introduction to Probabilistic Programming Jan-Willem van de Meent Brooks Paige Hongseok Yang Frank Wood GP 48 196 0 27 Sep 2018
Importance Weighting and Variational Inference Justin Domke Daniel Sheldon 39 107 0 27 Aug 2018
Tensor Monte Carlo: particle methods for the GPU era Laurence Aitchison BDL DRL 39 13 0 22 Jun 2018
Hamiltonian Variational Auto-Encoder Anthony L. Caterini Arnaud Doucet Dino Sejdinovic BDL DRL 37 96 0 29 May 2018
Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors Rico Jonschkowski Divyam Rastogi Oliver Brock 48 135 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
Implicit Reparameterization Gradients Michael Figurnov S. Mohamed A. Mnih BDL 86 231 0 22 May 2018
Tighter Variational Bounds are Not Necessarily Better Tom Rainforth Adam R. Kosiorek T. Le Chris J. Maddison Maximilian Igl Frank Wood Yee Whye Teh DRL 112 197 0 13 Feb 2018
Variational Sequential Monte Carlo C. A. Naesseth Scott W. Linderman Rajesh Ranganath David M. Blei BDL 152 214 0 31 May 2017
Auto-Encoding Sequential Monte Carlo T. Le Maximilian Igl Tom Rainforth Tom Jin Frank Wood BDL DRL 198 151 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 130 210 0 25 May 2017
Variational Boosting: Iteratively Refining Posterior Approximations Andrew C. Miller N. Foti Ryan P. Adams 43 125 0 20 Nov 2016
Categorical Reparameterization with Gumbel-Softmax Eric Jang S. Gu Ben Poole BDL 221 5,323 0 03 Nov 2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison A. Mnih Yee Whye Teh BDL 111 2,523 0 02 Nov 2016
Structured Inference Networks for Nonlinear State Space Models Rahul G. Krishnan Uri Shalit David Sontag BDL 75 454 0 30 Sep 2016
Stochastic Backpropagation through Mixture Density Distributions Alex Graves BDL 74 44 0 19 Jul 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 183 4,748 0 04 Jan 2016
The Variational Gaussian Process Dustin Tran Rajesh Ranganath David M. Blei BDL 64 184 0 20 Nov 2015
Hierarchical Variational Models Rajesh Ranganath Dustin Tran David M. Blei DRL VLM 60 334 0 07 Nov 2015
Importance Weighted Autoencoders Yuri Burda Roger C. Grosse Ruslan Salakhutdinov BDL 208 1,240 0 01 Sep 2015
Variational Gaussian Copula Inference Shaobo Han X. Liao David B. Dunson Lawrence Carin 53 56 0 19 Jun 2015
A Recurrent Latent Variable Model for Sequential Data Junyoung Chung Kyle Kastner Laurent Dinh Kratarth Goel Aaron Courville Yoshua Bengio DRL BDL 70 1,250 0 07 Jun 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 258 4,143 0 21 May 2015
Nested Sequential Monte Carlo Methods C. A. Naesseth Fredrik Lindsten Thomas B. Schon 408 84 0 09 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 896 149,474 0 22 Dec 2014
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 372 16,962 0 20 Dec 2013
Toward Practical N2 Monte Carlo: the Marginal Particle Filter Mike Klaas Nando de Freitas Arnaud Doucet 32 139 0 04 Jul 2012
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription Nicolas Boulanger-Lewandowski Yoshua Bengio Pascal Vincent 116 700 0 27 Jun 2012