Mode Coverage in Normalizing Flow Boltzmann Generators via Log-Ratio Variation
Normalizing flow Boltzmann generators retain a tractable pushforward density, but training with forward KL depends on target samples that may be biased or omit modes. As a result, a flow can miss target mass while its observed importance weights give a high effective sample size. We introduce the log-ratio variation $\X_ω$, the mean absolute pairwise difference of the target-to-pushforward log-density ratio under a weighting measure $ω$, and use it to define KLXX, a new loss function. Two log-ratio variations are added to the forward KL (denoted by the two X's): one weighted by the target to improve accuracy, the other by a mixture of quench and temper samples with pushforward samples to ...
| Authors | Qi Feng, Rongjie Lai, Di Qi et al. |
|---|---|
| Primary category | stat.ML |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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