Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding the Limitations of Variational Mutual Information
  Estimators   Variational approaches based on neural networks are showing promise for
estimating mutual information (MI) between high dimensional variables. However,
they can be difficult to use in practice due to poorly understood bias/variance
tradeoffs. We theoretically show that, under some conditions, estimators such
as MINE exhibit variance that could grow exponentially with the true amount of
underlying MI. We also empirically demonstrate that existing estimators fail to
satisfy basic self-consistency properties of MI, such as data processing and
additivity under independence. Based on a unified perspective of variational
approaches, we develop a new estimator that focuses on variance reduction.
Empirical results on standard benchmark tasks demonstrate that our proposed
estimator exhibits improved bias-variance trade-offs on standard benchmark
tasks.

7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat