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Inference Networks for Sequential Monte Carlo in Graphical Models

22 February 2016

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

48 / 48 papers shown

Title
Amortized In-Context Bayesian Posterior Estimation Sarthak Mittal Niels Leif Bracher Guillaume Lajoie P. Jaini Marcus A. Brubaker 114 2 0 10 Feb 2025
Foundation Inference Models for Markov Jump Processes David Berghaus K. Cvejoski Patrick Seifner C. Ojeda Ramses J. Sanchez 101 1 0 10 Jun 2024
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support Tim Reichelt C. Ong Tom Rainforth 63 2 0 01 Nov 2023
Truncated proposals for scalable and hassle-free simulation-based inference Michael Deistler P. J. Gonçalves Jakob H Macke 153 51 0 10 Oct 2022
Estimators of Entropy and Information via Inference in Probabilistic Models Feras A. Saad Marco F. Cusumano-Towner Vikash K. Mansinghka 56 4 0 24 Feb 2022
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems Juan Kuntz F. R. Crucinio A. M. Johansen 83 11 0 29 Oct 2021
Accelerating Metropolis-Hastings with Lightweight Inference Compilation Feynman T. Liang Nimar S. Arora N. Tehrani Y. Li Michael Tingley E. Meijer 50 0 0 23 Oct 2020
Semi-parametric $γ$ -ray modeling with Gaussian processes and variational inference S. Mishra-Sharma Kyle Cranmer MedIm 102 7 0 20 Oct 2020
Markovian Score Climbing: Variational Inference with KL(p\|\|q) C. A. Naesseth Fredrik Lindsten David M. Blei 189 55 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 91 93 0 12 Feb 2020
The frontier of simulation-based inference Kyle Cranmer Johann Brehmer Gilles Louppe AI4CE 266 857 0 04 Nov 2019
Neural Density Estimation and Likelihood-free Inference George Papamakarios BDL DRL 100 47 0 29 Oct 2019
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming Yura N. Perov L. Graham Kostis Gourgoulias Jonathan G. Richens Ciarán M. Gilligan-Lee Adam Baker Saurabh Johri LRM 60 17 0 17 Oct 2019
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs R. Walecki Kostis Gourgoulias Adam Baker Chris Hart Chris Lucas Max Zwiessele A. Buchard Maria Lomeli Yura N. Perov Saurabh Johri UQCV 51 0 0 16 Oct 2019
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning Johann Brehmer S. Mishra-Sharma Joeri Hermans Gilles Louppe Kyle Cranmer 112 74 0 04 Sep 2019
MadMiner: Machine learning-based inference for particle physics Johann Brehmer F. Kling Irina Espejo Kyle Cranmer 81 115 0 24 Jul 2019
Effective LHC measurements with matrix elements and machine learning Johann Brehmer Kyle Cranmer Irina Espejo F. Kling Gilles Louppe J. Pavez 81 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 120 128 0 15 May 2019
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs Aditya Sanjay Paliwal Felix Gimeno Vinod Nair Yujia Li Miles Lubin Pushmeet Kohli Oriol Vinyals OffRL GNN 99 67 0 07 May 2019
Elements of Sequential Monte Carlo C. A. Naesseth Fredrik Lindsten Thomas B. Schon 75 97 0 12 Mar 2019
Amortized Bayesian inference for clustering models Ari Pakman Liam Paninski 21 6 0 24 Nov 2018
Universal Marginalizer for Amortised Inference and Embedding of Generative Models R. Walecki A. Buchard Kostis Gourgoulias Chris Hart Maria Lomeli Alexandre Khae Wu Navarro Max Zwiessele Yura N. Perov Saurabh Johri BDL 70 2 0 12 Nov 2018
An Introduction to Probabilistic Programming Jan-Willem van de Meent Brooks Paige Hongseok Yang Frank Wood GP 88 200 0 27 Sep 2018
Likelihood-free inference with an improved cross-entropy estimator M. Stoye Johann Brehmer Gilles Louppe J. Pavez Kyle Cranmer FedML UQCV BDL 163 48 0 02 Aug 2018
Constructing Deep Neural Networks by Bayesian Network Structure Learning R. Y. Rohekar Shami Nisimov Yaniv Gurwicz G. Koren Gal Novik BDL 130 26 0 24 Jun 2018
Mining gold from implicit models to improve likelihood-free inference Johann Brehmer Gilles Louppe J. Pavez Kyle Cranmer AI4CE TPM 188 181 0 30 May 2018
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow T. Le Adam R. Kosiorek N. Siddharth Yee Whye Teh Frank Wood BDL 60 23 0 26 May 2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows George Papamakarios D. Sterratt Iain Murray BDL 552 370 0 18 May 2018
Learning Approximate Inference Networks for Structured Prediction Lifu Tu Kevin Gimpel BDL 54 53 0 09 Mar 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 210 198 0 13 Feb 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 134 47 0 01 Dec 2017
ZhuSuan: A Library for Bayesian Deep Learning Jiaxin Shi Jianfei Chen Jun Zhu Shengyang Sun Yucen Luo Yihong Gu Yuhao Zhou UQCV BDL 82 43 0 18 Sep 2017
Meta-Learning MCMC Proposals Tongzhou Wang Yi Wu David A. Moore Stuart J. Russell BDL 80 2 0 21 Aug 2017
Learning to Infer Graphics Programs from Hand-Drawn Images Kevin Ellis Daniel E. Ritchie Armando Solar-Lezama J. Tenenbaum NAI 104 231 0 30 Jul 2017
Learning to Draw Samples with Amortized Stein Variational Gradient Descent Yihao Feng Dilin Wang Qiang Liu GAN BDL 83 82 0 20 Jul 2017
Variational Sequential Monte Carlo C. A. Naesseth Scott W. Linderman Rajesh Ranganath David M. Blei BDL 301 215 0 31 May 2017
Auto-Encoding Sequential Monte Carlo T. Le Maximilian Igl Tom Rainforth Tom Jin Frank Wood BDL DRL 345 153 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 260 210 0 25 May 2017
Masked Autoregressive Flow for Density Estimation George Papamakarios Theo Pavlakou Iain Murray 234 1,361 0 19 May 2017
Translating Neuralese Jacob Andreas Anca Dragan Dan Klein 142 58 0 23 Apr 2017
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning T. Le A. G. Baydin R. Zinkov Frank Wood SyDa OOD 160 89 0 02 Mar 2017
Bayesian Probabilistic Numerical Methods Jon Cockayne Chris J. Oates T. Sullivan Mark Girolami 106 166 0 13 Feb 2017
Learning to superoptimize programs - Workshop Version Rudy Bunel Alban Desmaison M. P. Kumar Philip Torr Pushmeet Kohli 143 10 0 04 Dec 2016
Two Methods For Wild Variational Inference Qiang Liu Yihao Feng BDL 138 19 0 30 Nov 2016
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Dilin Wang Qiang Liu GAN BDL 151 119 0 06 Nov 2016
Inference Compilation and Universal Probabilistic Programming T. Le A. G. Baydin Frank Wood UQCV 219 143 0 31 Oct 2016
Deep Amortized Inference for Probabilistic Programs Daniel E. Ritchie Paul Horsfall Noah D. Goodman TPM 111 82 0 18 Oct 2016
Fast $ε$ -free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios Iain Murray TPM 189 158 0 20 May 2016