BayesFlow: Learning complex stochastic models with invertible neural networks

13 March 2020

Papers citing "BayesFlow: Learning complex stochastic models with invertible neural networks"

27 / 27 papers shown

Title
Robust Simulation-Based Inference under Missing Data via Neural Processes Yogesh Verma Ayush Bharti Vikas K. Garg 72 0 0 03 Mar 2025
Expert-elicitation method for non-parametric joint priors using normalizing flows F. Bockting Stefan T. Radev Paul-Christian Bürkner BDL 105 1 0 24 Nov 2024
Compositional simulation-based inference for time series Manuel Gloeckler S. Toyota Kenji Fukumizu Jakob H Macke 58 2 0 05 Nov 2024
Inverse decision-making using neural amortized Bayesian actors Dominik Straub Tobias F. Niehues Jan Peters Constantin Rothkopf 41 0 0 04 Sep 2024
Amortized Bayesian Multilevel Models Daniel Habermann Marvin Schmitt Lars Kühmichel Andreas Bulling Stefan T. Radev Paul-Christian Bürkner 75 4 0 23 Aug 2024
Variational Bayesian Optimal Experimental Design with Normalizing Flows Jiayuan Dong Christian L. Jacobsen Mehdi Khalloufi Maryam Akram Wanjiao Liu Karthik Duraisamy Xun Huan BDL 59 6 0 08 Apr 2024
Improved off-policy training of diffusion samplers Marcin Sendera Minsu Kim Sarthak Mittal Pablo Lemos Luca Scimeca Jarrid Rector-Brooks Alexandre Adam Yoshua Bengio Nikolay Malkin OffRL 71 18 0 07 Feb 2024
InvertibleNetworks.jl: A Julia package for scalable normalizing flows Rafael Orozco Philipp A. Witte M. Louboutin Ali Siahkoohi G. Rizzuti Bas Peters Felix J. Herrmann 40 9 0 20 Dec 2023
Amortized Bayesian Decision Making for simulation-based models Mila Gorecki Jakob H. Macke Michael Deistler 32 1 0 05 Dec 2023
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks Matthew Sainsbury-Dale A. Zammit‐Mangion J. Richards Raphael Huser 41 15 0 04 Oct 2023
Lifting Architectural Constraints of Injective Flows Peter Sorrenson Felix Dräxler Armand Rousselot Sander Hummerich Leandro Zimmerman Ullrich Kothe TPM AI4CE 39 8 0 02 Jun 2023
Physics-informed neural networks for solving forward and inverse problems in complex beam systems Taniya Kapoor Hongrui Wang A. Núñez R. Dollevoet AI4CE PINN 28 46 0 02 Mar 2023
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference Ryan P. Kelly David J. Nott David T. Frazier D. Warne Christopher C. Drovandi 38 11 0 31 Jan 2023
Certified Invertibility in Neural Networks via Mixed-Integer Programming Tianqi Cui Tom S. Bertalan George J. Pappas M. Morari Ioannis G. Kevrekidis Mahyar Fazlyab AAML 29 2 0 27 Jan 2023
Compositional Score Modeling for Simulation-based Inference Tomas Geffner George Papamakarios A. Mnih 72 25 0 28 Sep 2022
Reliable amortized variational inference with physics-based latent distribution correction Ali Siahkoohi G. Rizzuti Rafael Orozco Felix J. Herrmann 41 28 0 24 Jul 2022
Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization Lorenzo Pacchiardi Ritabrata Dutta TPM BDL UQCV GAN 31 18 0 31 May 2022
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks Marvin Schmitt Paul-Christian Bürkner Ullrich Kothe Stefan T. Radev 39 35 0 16 Dec 2021
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization Lorenzo Pacchiardi Rilwan A. Adewoyin P. Dueben Ritabrata Dutta AI4TS 24 21 0 15 Dec 2021
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models Ling Guo Hao Wu Tao Zhou AI4CE 14 45 0 30 Aug 2021
Neural Networks for Parameter Estimation in Intractable Models Amanda Lenzi J. Bessac J. Rudi Michael L. Stein BDL 33 50 0 29 Jul 2021
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models Louis Rouillard Demian Wassermann 41 2 0 23 Jun 2021
Understanding Event-Generation Networks via Uncertainties Marco Bellagente Manuel Haussmann Michel Luchmann Tilman Plehn BDL 43 55 0 09 Apr 2021
Sequential Neural Posterior and Likelihood Approximation Samuel Wiqvist J. Frellsen Umberto Picchini BDL 39 33 0 12 Feb 2021
Benchmarking Simulation-Based Inference Jan-Matthis Lueckmann Jan Boelts David S. Greenberg P. J. Gonçalves Jakob H. Macke 104 186 0 12 Jan 2021
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation Mattias Åkesson Prashant Singh Fredrik Wrede Andreas Hellander BDL 34 33 0 31 Jan 2020
The Neural Moving Average Model for Scalable Variational Inference of State Space Models Tom Ryder D. Prangle Andrew Golightly Isaac Matthews BDL AI4TS 21 6 0 02 Oct 2019