Poster (Scientific congresses and symposiums)
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Lezcano Casado, Mario; Gunes Baydin, Atilim; Martinez Rubio, David et al.
2017Deep Learning for Physical Sciences workshop, NeurIPS 2018
 

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Keywords :
Computer Science - Artificial Intelligence; Physics - Data Analysis; Statistics and Probability; 68T37; 68T05; 62P35; G.3; I.2.6; J.2
Abstract :
[en] We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to cosmology, low-level mechanistic components are composed to create complex generative models. These models lead to intractable likelihoods and are typically non-differentiable, which poses challenges for traditional approaches to inference. We extend previous work in "inference compilation", which combines universal probabilistic programming and deep learning methods, to large-scale scientific simulators, and introduce a C++ based probabilistic programming library called CPProb. We successfully use CPProb to interface with SHERPA, a large code-base used in particle physics. Here we describe the technical innovations realized and planned for this library.
Disciplines :
Computer science
Author, co-author :
Lezcano Casado, Mario
Gunes Baydin, Atilim
Martinez Rubio, David
Le, Tuan Anh
Wood, Frank
Heinrich, Lukas
Louppe, Gilles  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
Cranmer, Kyle
Ng, Karen
Bhimji, Wahid
Prabhat
Language :
English
Title :
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Publication date :
08 December 2017
Event name :
Deep Learning for Physical Sciences workshop, NeurIPS 2018
Event place :
Montreal, Canada
Event date :
December 8, 2017
Audience :
International
Commentary :
7 pages, 2 figures
Available on ORBi :
since 28 June 2018

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