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The frontier of simulation-based inference

4 November 2019

Papers citing "The frontier of simulation-based inference"

37 / 337 papers shown

Title
Hierarchical clustering in particle physics through reinforcement learning Johann Brehmer S. Macaluso D. Pappadopulo Kyle Cranmer 26 6 0 16 Nov 2020
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks N. Jeffrey Benjamin Dan Wandelt 70 39 0 11 Nov 2020
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference M. Vandegar Michael Kagan Antoine Wehenkel Gilles Louppe 72 28 0 11 Nov 2020
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization Arnaud Delaunoy Antoine Wehenkel T. Hinderer S. Nissanke Christoph Weniger A. Williamson Gilles Louppe 94 30 0 24 Oct 2020
ABC-Di: Approximate Bayesian Computation for Discrete Data I. Auzina Jakub M. Tomczak 29 0 0 19 Oct 2020
Error-guided likelihood-free MCMC Volodimir Begy Erich Schikuta 63 3 0 13 Oct 2020
Simulation-based inference methods for particle physics Johann Brehmer Kyle Cranmer AI4CE 126 22 0 13 Oct 2020
Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles Ushnish Sengupta Maximilian L. Croci M. Juniper 12 3 0 11 Oct 2020
Automating Inference of Binary Microlensing Events with Neural Density Estimation Keming 名 Zhang 张可 J. Bloom B. Gaudi F. Lanusse C. Lam Jessica R. Lu 21 1 0 08 Oct 2020
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany Stefan T. Radev Frederik Graw Simiao Chen N. Mutters V. Eichel T. Bärnighausen Ullrich Kothe 77 32 0 01 Oct 2020
Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae Ewelina Węglarz-Tomczak Jakub M. Tomczak A. E. Eiben S. Brul 14 0 0 19 Sep 2020
Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modeling Timothy Rumbell Jaimit Parikh J. Kozloski V. Gurev 48 6 0 17 Sep 2020
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels Alexander Lavin BDL MedIm 79 9 0 16 Sep 2020
Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation Jeevan Devaranjan Amlan Kar Sanja Fidler 76 89 0 20 Aug 2020
Learning Insulin-Glucose Dynamics in the Wild Andrew C. Miller N. Foti E. Fox AI4TS 35 20 0 06 Aug 2020
Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review T. Dorigo P. D. Castro 57 14 0 17 Jul 2020
Variational Autoencoding of PDE Inverse Problems Daniel J. Tait Theodoros Damoulas AI4CE 49 12 0 28 Jun 2020
Technology Readiness Levels for AI & ML Alexander Lavin Ajay Sharma VLM 111 110 0 21 Jun 2020
Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference Ricardo Baptista Bamdad Hosseini Nikola B. Kovachki Youssef Marzouk OT GAN 98 24 0 11 Jun 2020
Simulation-Based Inference for Global Health Decisions Christian Schroeder de Witt Bradley Gram-Hansen Nantas Nardelli Andrew Gambardella R. Zinkov ... N. Siddharth A. B. Espinosa-González A. Darzi Philip Torr A. G. Baydin AI4CE 61 5 0 14 May 2020
Amortized Bayesian Inference for Models of Cognition Stefan T. Radev A. Voss Eva Marie Wieschen Paul-Christian Bürkner 63 5 0 08 May 2020
Amortized Bayesian model comparison with evidential deep learning Stefan T. Radev Marco D’Alessandro U. Mertens A. Voss Ullrich Kothe Paul-Christian Bürkner BDL 85 34 0 22 Apr 2020
Flows for simultaneous manifold learning and density estimation Johann Brehmer Kyle Cranmer DRL AI4CE 108 163 0 31 Mar 2020
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters Stefan Wunsch Simon Jörger R. Wolf G. Quast 65 20 0 16 Mar 2020
BayesFlow: Learning complex stochastic models with invertible neural networks Stefan T. Radev U. Mertens A. Voss Lynton Ardizzone Ullrich Kothe BDL 312 197 0 13 Mar 2020
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting Niccolò Dalmasso Rafael Izbicki Ann B. Lee 66 21 0 24 Feb 2020
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems Hans Kersting N. Krämer Martin Schiegg Christian Daniel Michael Tiemann Philipp Hennig 70 21 0 21 Feb 2020
Black-Box Optimization with Local Generative Surrogates S. Shirobokov V. Belavin Michael Kagan Andrey Ustyuzhanin A. G. Baydin 40 3 0 11 Feb 2020
On Contrastive Learning for Likelihood-free Inference Conor Durkan Iain Murray George Papamakarios BDL 233 123 0 10 Feb 2020
Differential Evolution with Reversible Linear Transformations Jakub M. Tomczak Ewelina Węglarz-Tomczak A. E. Eiben 67 19 0 07 Feb 2020
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation Mattias Åkesson Prashant Singh Fredrik Wrede Andreas Hellander BDL 107 33 0 31 Jan 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 217 1,719 0 05 Dec 2019
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication) Marek Rosa O. Afanasjeva Simon Andersson Joseph Davidson N. Guttenberg Petr Hlubucek Martin Poliak Jaroslav Vítků Jan Feyereisl 72 10 0 03 Dec 2019
Coupling techniques for nonlinear ensemble filtering Alessio Spantini Ricardo Baptista Youssef Marzouk 115 76 0 30 Jun 2019
Stratified sampling and bootstrapping for approximate Bayesian computation Umberto Picchini R. Everitt 58 1 0 20 May 2019
Robust Approximate Bayesian Inference with Synthetic Likelihood David T. Frazier Christopher C. Drovandi 70 45 0 09 Apr 2019
Constraining Effective Field Theories with Machine Learning Johann Brehmer Kyle Cranmer Gilles Louppe J. Pavez AI4CE 129 152 0 30 Apr 2018