הצעת ניוון נוירונלית להמונטה-קרלו המונהגת על ידי המקדמים של המקדמים

תקציר מקורי באנגליתarXiv:2607.15682v3 Announce Type: replace Abstract: Learned dynamical proposals can generate configurations without providing a tractable endpoint density. Nonequilibrium path probabilities offer a way to correct such proposals, but correction alone does not determine their ability to connect separated regions. We introduce Neural Non-Equilibrium Hamiltonian Monte Carlo (NHMC), which combines conditional momentum distributions with reversible, volume-preserving dynamics. The forward--reverse path ratio gives the work used for training, importance weighting, normalizer estimation, and Metropolis correction. We then construct a configuration-space round-trip kernel whose reverse and forward paths share an intermediate configuration. Conditional on that configuration, its path-record update i