Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GLAD: Learning Sparse Graph Recovery   Recovering sparse conditional independence graphs from data is a fundamental
problem in machine learning with wide applications. A popular formulation of
the problem is an $\ell_1$ regularized maximum likelihood estimation. Many
convex optimization algorithms have been designed to solve this formulation to
recover the graph structure. Recently, there is a surge of interest to learn
algorithms directly based on data, and in this case, learn to map empirical
covariance to the sparse precision matrix. However, it is a challenging task in
this case, since the symmetric positive definiteness (SPD) and sparsity of the
matrix are not easy to enforce in learned algorithms, and a direct mapping from
data to precision matrix may contain many parameters. We propose a deep
learning architecture, GLAD, which uses an Alternating Minimization (AM)
algorithm as our model inductive bias, and learns the model parameters via
supervised learning. We show that GLAD learns a very compact and effective
model for recovering sparse graphs from data.

13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys