Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Entropic Determinants   The ability of many powerful machine learning algorithms to deal with large
data sets without compromise is often hampered by computationally expensive
linear algebra tasks, of which calculating the log determinant is a canonical
example. In this paper we demonstrate the optimality of Maximum Entropy methods
in approximating such calculations. We prove the equivalence between mean value
constraints and sample expectations in the big data limit, that Covariance
matrix eigenvalue distributions can be completely defined by moment information
and that the reduction of the self entropy of a maximum entropy proposal
distribution, achieved by adding more moments reduces the KL divergence between
the proposal and true eigenvalue distribution. We empirically verify our
results on a variety of SparseSuite matrices and establish best practices.

35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex