Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comment on "Solving Statistical Mechanics Using VANs": Introducing
  saVANt - VANs Enhanced by Importance and MCMC Sampling   In this comment on "Solving Statistical Mechanics Using Variational
Autoregressive Networks" by Wu et al., we propose a subtle yet powerful
modification of their approach. We show that the inherent sampling error of
their method can be corrected by using neural network-based MCMC or importance
sampling which leads to asymptotically unbiased estimators for physical
quantities. This modification is possible due to a singular property of VANs,
namely that they provide the exact sample probability. With these
modifications, we believe that their method could have a substantially greater
impact on various important fields of physics, including strongly-interacting
field theories and statistical physics.

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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: eess
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
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The categories of this paper are: comp-gas