Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning of Thermodynamic Observables in the Presence of Mode
  Collapse   Estimating the free energy, as well as other thermodynamic observables, is a
key task in lattice field theories. Recently, it has been pointed out that deep
generative models can be used in this context [1]. Crucially, these models
allow for the direct estimation of the free energy at a given point in
parameter space. This is in contrast to existing methods based on Markov chains
which generically require integration through parameter space. In this
contribution, we will review this novel machine-learning-based estimation
method. We will in detail discuss the issue of mode collapse and outline
mitigation techniques which are particularly suited for applications at finite
temperature.

23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph