Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged
  Gradient Method for Stochastic Optimization   We introduce MADGRAD, a novel optimization method in the family of AdaGrad
adaptive gradient methods. MADGRAD shows excellent performance on deep learning
optimization problems from multiple fields, including classification and
image-to-image tasks in vision, and recurrent and bidirectionally-masked models
in natural language processing. For each of these tasks, MADGRAD matches or
outperforms both SGD and ADAM in test set performance, even on problems for
which adaptive methods normally perform poorly.

35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
18
The categories of this paper are: econ
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph