Gradient estimators for normalising flows

• URL: http://arxiv.org/abs/2202.01314v1
• Date: Wed, 2 Feb 2022 22:37:58 GMT
• Title: Gradient estimators for normalising flows
• Authors: Piotr Bialas and Piotr Korcyl and Tomasz Stebel
• Abstract summary: A machine learning approach to Monte-Carlo simulations called Neural Markov Chain Monte-Carlo is gaining traction. We present another gradient estimator that avoids the calculation, thus potentially speeding up training for models with more complicated actions. We also study the statistical properties of several gradient estimators and show that our formulation leads to better training results.
• Score: 0.05156484100374058
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently a machine learning approach to Monte-Carlo simulations called Neural Markov Chain Monte-Carlo (NMCMC) is gaining traction. In its most popular form it uses the neural networks to construct normalizing flows which are then trained to approximate the desired target distribution. As this distribution is usually defined via a Hamiltonian or action, the standard learning algorithm requires estimation of the action gradient with respect to the fields. In this contribution we present another gradient estimator (and the corresponding [PyTorch implementation) that avoids this calculation, thus potentially speeding up training for models with more complicated actions. We also study the statistical properties of several gradient estimators and show that our formulation leads to better training results.

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