Deep Probabilistic Surrogate Networks for Universal Simulator Approximation

By Andreas Munk, Adam Scibior, Atilim Gunes Baydin, Andrew Stewart, G. Fernlund, A. Poursartip, Frank D. Wood. 2019

15 citations recorded by the source corpus.

Subfields: Generative Models, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Generative Models

1. Gilles Louppe
2. J. Brehmer
3. Kyle Cranmer
4. Sergey I. Nikolenko
5. Joeri Hermans

In Probabilistic & Bayesian Methods

1. J. Brehmer
2. Gilles Louppe
3. Kyle Cranmer
4. Joeri Hermans
5. Juan Pavez
6. Volodimir Begy
7. Sergey I. Nikolenko
8. George Papamakarios

Citations in the corpus

• The frontier of simulation-based inference (2019, background): “Recently attempts have been made to automate the process of creating surrogates that mimic the simulation (70).”