Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Descent Analysis of Representation Learning Algorithms   Although stochastic approximation learning methods have been widely used in
the machine learning literature for over 50 years, formal theoretical analyses
of specific machine learning algorithms are less common because stochastic
approximation theorems typically possess assumptions which are difficult to
communicate and verify. This paper presents a new stochastic approximation
theorem for state-dependent noise with easily verifiable assumptions applicable
to the analysis and design of important deep learning algorithms including:
adaptive learning, contrastive divergence learning, stochastic descent
expectation maximization, and active learning.

26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th