Atılım Güneş Baydin: "Universal Probabilistic Programming in Simulators" скачать в хорошем качестве

Atılım Güneş Baydin: "Universal Probabilistic Programming in Simulators"

Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Universal Probabilistic Programming in Simulators" Atılım Güneş Baydin, University of Oxford Abstract: We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline. Institute for Pure and Applied Mathematics, UCLA October 15, 2019 For more information: http://www.ipam.ucla.edu/mlpws2