Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Nonconvex Optimization with Large Minibatches   We study stochastic optimization of nonconvex loss functions, which are
typical objectives for training neural networks. We propose stochastic
approximation algorithms which optimize a series of regularized, nonlinearized
losses on large minibatches of samples, using only first-order gradient
information. Our algorithms provably converge to an approximate critical point
of the expected objective with faster rates than minibatch stochastic gradient
descent, and facilitate better parallelization by allowing larger minibatches.

12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys