MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics
The paper introduces MetaDNS, a framework that integrates well-tempered metadynamics into discrete neural samplers to overcome mode collapse and enable efficient exploration of high-energy barriers for accurate free energy estimation in complex discrete distributions.