Variational Sequential Monte Carlo

31 May 2017

Papers citing "Variational Sequential Monte Carlo"

50 / 63 papers shown

Title
Trust-Region Twisted Policy Improvement Joery A. de Vries Jinke He Yaniv Oren M. Spaan OffRL LRM 35 0 0 08 Apr 2025
Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics Hanming Yang A. Moretti Sebastian Macaluso Philippe Chlenski C. A. Naesseth I. Pe’er BDL 47 1 0 03 Jan 2025
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Benjamin Cox Santiago Segarra Victor Elvira 81 0 0 23 Nov 2024
Meta-Dynamical State Space Models for Integrative Neural Data Analysis Ayesha Vermani Josue Nassar Hyungju Jeon Matthew Dowling Il Memming Park 39 1 0 07 Oct 2024
Inferring stochastic low-rank recurrent neural networks from neural data Matthijs Pals A Erdem Sağtekin Felix Pei Manuel Gloeckler Jakob H Macke 43 7 0 24 Jun 2024
Revisiting semi-supervised training objectives for differentiable particle filters Jiaxi Li John-Joseph Brady Xiongjie Chen Yunpeng Li 28 1 0 02 May 2024
Provably Scalable Black-Box Variational Inference with Structured Variational Families Joohwan Ko Kyurae Kim W. Kim Jacob R. Gardner BDL 33 2 0 19 Jan 2024
Reparameterized Variational Rejection Sampling M. Jankowiak Du Phan DRL BDL 24 1 0 26 Sep 2023
Massively Parallel Reweighted Wake-Sleep Thomas Heap Gavin Leech Laurence Aitchison BDL 24 2 0 18 May 2023
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems Andreas Look M. Kandemir Barbara Rakitsch Jan Peters BDL 36 6 0 02 May 2023
Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers Johannes Zenn Robert Bamler 39 3 0 27 Apr 2023
U-Statistics for Importance-Weighted Variational Inference Javier Burroni Kenta Takatsu Justin Domke Daniel Sheldon 18 1 0 27 Feb 2023
Statistical Distance Based Deterministic Offspring Selection in SMC Methods Oskar Kviman Hazal Koptagel Harald Melin J. Lagergren 22 0 0 23 Dec 2022
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving Angad Singh Omar Makhlouf Maximilian Igl Joao Messias Arnaud Doucet Shimon Whiteson 49 2 0 14 Dec 2022
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method A. Wigren Johan Wågberg Fredrik Lindsten A. Wills Thomas B. Schon 24 10 0 26 Oct 2022
Efficient variational approximations for state space models Rubén Loaiza-Maya D. Nibbering 8 1 0 20 Oct 2022
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning P. Becker Gerhard Neumann 30 9 0 17 Oct 2022
A Variational Perspective on Generative Flow Networks Heiko Zimmermann Fredrik Lindsten Jan-Willem van de Meent C. A. Naesseth 22 32 0 14 Oct 2022
Optimization of Annealed Importance Sampling Hyperparameters Shirin Goshtasbpour Fernando Perez-Cruz 27 1 0 27 Sep 2022
SIXO: Smoothing Inference with Twisted Objectives Dieterich Lawson Allan Raventós Andrew Warrington Scott W. Linderman BDL 15 15 0 13 Jun 2022
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother Adrien Corenflos Nicolas Chopin Simo Särkkä 29 6 0 04 Feb 2022
Continual Repeated Annealed Flow Transport Monte Carlo A. G. Matthews Michael Arbel Danilo Jimenez Rezende Arnaud Doucet OT 37 46 0 31 Jan 2022
Surrogate Likelihoods for Variational Annealed Importance Sampling M. Jankowiak Du Phan BDL 35 13 0 22 Dec 2021
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors Shivendra Agrawal Hwanwoo Kim D. Sanz-Alonso A. Strang 6 10 0 26 Nov 2021
Online Variational Filtering and Parameter Learning Andrew Campbell Yuyang Shi Tom Rainforth Arnaud Doucet BDL 32 21 0 26 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
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling Xin Huang Guy Rosman Igor Gilitschenski A. Jasour Stephen G. McGill J. Leonard B. Williams 26 26 0 05 Oct 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 31 266 0 21 Jul 2021
On Incorporating Inductive Biases into VAEs Ning Miao Emile Mathieu N. Siddharth Yee Whye Teh Tom Rainforth CML DRL 30 10 0 25 Jun 2021
Nested Variational Inference Heiko Zimmermann Hao Wu Babak Esmaeili Jan-Willem van de Meent BDL 29 20 0 21 Jun 2021
Differentiable Particle Filtering without Modifying the Forward Pass Adam Scibior Frank Wood 28 19 0 18 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
Stein Variational Gradient Descent: many-particle and long-time asymptotics Nikolas Nusken D. M. Renger 29 22 0 25 Feb 2021
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport Adrien Corenflos James Thornton George Deligiannidis Arnaud Doucet OT 43 66 0 15 Feb 2021
Annealed Flow Transport Monte Carlo Michael Arbel A. G. Matthews Arnaud Doucet 36 69 0 15 Feb 2021
End-To-End Semi-supervised Learning for Differentiable Particle Filters Hao Wen Xiongjie Chen Georgios Papagiannis Conghui Hu Yunpeng Li 33 17 0 11 Nov 2020
Variational Dynamic Mixtures Chen Qiu Stephan Mandt Maja R. Rudolph BDL AI4TS 16 2 0 20 Oct 2020
Variational Filtering with Copula Models for SLAM John D. Martin Kevin Doherty Caralyn Cyr Brendan Englot J. Leonard 27 3 0 02 Aug 2020
An invitation to sequential Monte Carlo samplers Chenguang Dai J. Heng Pierre E. Jacob N. Whiteley 52 65 0 23 Jul 2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal Daniel Sheldon Justin Domke TPM BDL 8 38 0 18 Jun 2020
Markovian Score Climbing: Variational Inference with KL(p\|\|q) C. A. Naesseth Fredrik Lindsten David M. Blei 123 54 0 23 Mar 2020
Lazy object copy as a platform for population-based probabilistic programming Lawrence M. Murray 21 5 0 09 Jan 2020
Particle Smoothing Variational Objectives A. Moretti Zizhao Wang Luhuan Wu Iddo Drori I. Pe’er 26 10 0 20 Sep 2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation Justin Domke Daniel Sheldon 24 18 0 24 Jun 2019
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting Aditya Grover Jiaming Song Alekh Agarwal Kenneth Tran Ashish Kapoor Eric Horvitz Stefano Ermon 26 123 0 23 Jun 2019
Streaming Variational Monte Carlo Yuan Zhao Josue Nassar I. Jordan M. Bugallo Il Memming Park BDL 34 21 0 04 Jun 2019
Particle Filter Recurrent Neural Networks Xiao Ma Peter Karkus David Hsu Wee Sun Lee 14 82 0 30 May 2019
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces P. Becker Harit Pandya Gregor H. W. Gebhardt Cheng Zhao James Taylor Gerhard Neumann BDL 15 94 0 17 May 2019
Particle Flow Bayes' Rule Xinshi Chen H. Dai Le Song 16 9 0 02 Feb 2019
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler Duo Xu 26 2 0 03 Jan 2019