Variational Autoregressive Networks with probability priors
Monte Carlo methods are essential across diverse scientific fields, yet their efficiency is frequently hampered by critical slowing down-a sharp increase in autocorrelation times near phase transitions. Although deep learning approaches, such as neural-network-based samplers, have been proposed to alleviate this issue, they face another serious problem: the difficulty of training the models. This difficulty partially stems from the overly general nature of original machine-learning architectures, which often ignore underlying physical symmetries and force networks to relearn them from scratch. In this paper, we demonstrate that incorporating physical priors into the model significantly en...
| Authors | Piotr Białas, Piotr Korcyl, Tomasz Stebel et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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