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A Practical Guide to Statistical Distances for Evaluating Generative
  Models in Science

A Practical Guide to Statistical Distances for Evaluating Generative Models in Science

19 March 2024
Sebastian Bischoff
Alana Darcher
Michael Deistler
Richard Gao
Franziska Gerken
Manuel Gloeckler
L. Haxel
J. Kapoor
Janne K. Lappalainen
Jakob H Macke
Guy Moss
Matthijs Pals
Felix Pei
Rachel Rapp
A. E. Saugtekin
Cornelius Schroder
Auguste Schulz
Zinovia Stefanidi
Shoji Toyota
Linda Ulmer
Julius Vetter
    SyDa
ArXiv (abs)PDFHTML

Papers citing "A Practical Guide to Statistical Distances for Evaluating Generative Models in Science"

2 / 2 papers shown
Title
An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation
An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation
Yifei Xiong
Xiliang Yang
Sanguo Zhang
Zhijian He
281
2
0
17 Jan 2025
Sensitivity-Aware Amortized Bayesian Inference
Sensitivity-Aware Amortized Bayesian Inference
Lasse Elsemüller
Hans Olischläger
Marvin Schmitt
Paul-Christian Bürkner
Ullrich Kothe
Stefan T. Radev
118
9
0
17 Oct 2023
1