A research paper presenting Jeffreys Flow, a method for generating samples from complex probability distributions using Boltzmann generators combined with parallel tempering distillation. The technique addresses rare event sampling—a fundamental challenge in Monte Carlo methods and generative modeling.
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Jeffreys Flow: Robust Boltzmann Generators for Rare Event Sampling via Parallel Tempering Distillation
Jeffreys Flow combines Boltzmann generators with parallel tempering distillation to sample rare events more efficiently in complex probability distributions, advancing Monte Carlo methods.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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