Auto-Encoding Sequential Monte Carlo

29 May 2017

Papers citing "Auto-Encoding Sequential Monte Carlo"

37 / 37 papers shown

Title
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
Inferring stochastic low-rank recurrent neural networks from neural data Matthijs Pals A Erdem Sağtekin Felix Pei Manuel Gloeckler Jakob H Macke 38 6 0 24 Jun 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 32 3 0 27 Apr 2023
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving Angad Singh Omar Makhlouf Maximilian Igl Joao Messias Arnaud Doucet Shimon Whiteson 47 2 0 14 Dec 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 13 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
Learning to Assimilate in Chaotic Dynamical Systems Michael McCabe Jed Brown AI4TS 36 10 0 01 Nov 2021
Online Variational Filtering and Parameter Learning Andrew Campbell Yuyang Shi Tom Rainforth Arnaud Doucet BDL 30 21 0 26 Oct 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
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport Adrien Corenflos James Thornton George Deligiannidis Arnaud Doucet OT 43 66 0 15 Feb 2021
Multimodal Sensor Fusion with Differentiable Filters Michelle A. Lee Brent Yi Roberto Martín-Martín Silvio Savarese Jeannette Bohg 27 59 0 25 Oct 2020
Variational Dynamic Mixtures Chen Qiu Stephan Mandt Maja R. Rudolph BDL AI4TS 16 2 0 20 Oct 2020
Markovian Score Climbing: Variational Inference with KL(p\|\|q) C. A. Naesseth Fredrik Lindsten David M. Blei 123 54 0 23 Mar 2020
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models Yuge Shi Siddharth Narayanaswamy Brooks Paige Philip Torr DRL 27 266 0 08 Nov 2019
Particle Smoothing Variational Objectives A. Moretti Zizhao Wang Luhuan Wu Iddo Drori I. Pe’er 24 10 0 20 Sep 2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation Justin Domke Daniel Sheldon 18 18 0 24 Jun 2019
Streaming Variational Monte Carlo Yuan Zhao Josue Nassar I. Jordan M. Bugallo Il Memming Park BDL 34 21 0 04 Jun 2019
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler Duo Xu 18 2 0 03 Jan 2019
A General Method for Amortizing Variational Filtering Joseph Marino Milan Cvitkovic Yisong Yue 27 34 0 13 Nov 2018
The Variational Deficiency Bottleneck P. Banerjee Guido Montúfar 15 7 0 27 Oct 2018
A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling Zhijian Ou 31 16 0 05 Aug 2018
Deep Variational Reinforcement Learning for POMDPs Maximilian Igl L. Zintgraf T. Le Frank Wood Shimon Whiteson BDL OffRL 21 258 0 06 Jun 2018
Reparameterization Gradient for Non-differentiable Models Wonyeol Lee Hangyeol Yu Hongseok Yang DRL 22 30 0 01 Jun 2018
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers Marcel Hirt P. Dellaportas BDL 20 10 0 23 May 2018
Faithful Inversion of Generative Models for Effective Amortized Inference Stefan Webb Adam Goliñski R. Zinkov Siddharth Narayanaswamy Tom Rainforth Yee Whye Teh Frank Wood TPM 48 46 0 01 Dec 2017
Filtering Variational Objectives Chris J. Maddison Dieterich Lawson George Tucker N. Heess Mohammad Norouzi A. Mnih Arnaud Doucet Yee Whye Teh FedML 22 210 0 25 May 2017
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms Marco F. Cusumano-Towner Vikash K. Mansinghka 16 17 0 19 May 2017