Inference Networks for Sequential Monte Carlo in Graphical Models

22 February 2016

Papers citing "Inference Networks for Sequential Monte Carlo in Graphical Models"

18 / 18 papers shown

Title
Foundation Inference Models for Markov Jump Processes David Berghaus K. Cvejoski Patrick Seifner C. Ojeda Ramses J. Sanchez 34 1 0 10 Jun 2024
nbi: the Astronomer's Package for Neural Posterior Estimation Keming 名 Zhang 张可 Joshua S. Bloom Stéfan van der Walt N. Hernitschek 19 3 0 06 Dec 2023
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems Juan Kuntz F. R. Crucinio A. M. Johansen 19 11 0 29 Oct 2021
Semi-parametric $γ$ -ray modeling with Gaussian processes and variational inference S. Mishra-Sharma Kyle Cranmer MedIm 11 7 0 20 Oct 2020
Markovian Score Climbing: Variational Inference with KL(p\|\|q) C. A. Naesseth Fredrik Lindsten David M. Blei 110 54 0 23 Mar 2020
Targeted free energy estimation via learned mappings Peter Wirnsberger A. J. Ballard George Papamakarios Stuart Abercrombie S. Racanière Alexander Pritzel Danilo Jimenez Rezende Charles Blundell 11 86 0 12 Feb 2020
Effective LHC measurements with matrix elements and machine learning Johann Brehmer Kyle Cranmer Irina Espejo F. Kling Gilles Louppe J. Pavez 18 14 0 04 Jun 2019
Meta reinforcement learning as task inference Jan Humplik Alexandre Galashov Leonard Hasenclever Pedro A. Ortega Yee Whye Teh N. Heess OffRL 13 127 0 15 May 2019
Mining gold from implicit models to improve likelihood-free inference Johann Brehmer Gilles Louppe J. Pavez Kyle Cranmer AI4CE TPM 27 180 0 30 May 2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows George Papamakarios D. Sterratt Iain Murray BDL 33 358 0 18 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 D. Wood TPM 24 46 0 01 Dec 2017
Translating Neuralese Jacob Andreas Anca Dragan Dan Klein 21 58 0 23 Apr 2017
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning T. Le A. G. Baydin R. Zinkov Frank D. Wood SyDa OOD 25 89 0 02 Mar 2017
Bayesian Probabilistic Numerical Methods Jon Cockayne Chris J. Oates T. Sullivan Mark Girolami 13 164 0 13 Feb 2017
Learning to superoptimize programs - Workshop Version Rudy Bunel Alban Desmaison M. P. Kumar Philip H. S. Torr Pushmeet Kohli 21 10 0 04 Dec 2016
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Dilin Wang Qiang Liu GAN BDL 25 118 0 06 Nov 2016
Deep Amortized Inference for Probabilistic Programs Daniel E. Ritchie Paul Horsfall Noah D. Goodman TPM 13 81 0 18 Oct 2016
Fast $ε$ -free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios Iain Murray TPM 28 158 0 20 May 2016