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Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering

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Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability. We introduce a training algorithm based on Parallel Trajectory Tempering (PTT), which exploits the continuity of the optimization path to maintain equilibrium sampling throughout learning.

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arXiv Machine Learning
Jun 30

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps

arXiv:2606. 29110v1 Announce Type: new Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations.

By RuiKang OuYang, Hanlin Yu, Xinyue Ai, Yutong He, Nicholas M. Boffi, Pradeep Ravikumar, Jose Miguel Hernandez-Lobato, Max Simchowitz, Benjamin Kurt Miller, Omar Chehab