Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Generalization of Models Trained with SGD: Information-Theoretic
  Bounds and Implications   This paper follows up on a recent work of Neu et al. (2021) and presents some
new information-theoretic upper bounds for the generalization error of machine
learning models, such as neural networks, trained with SGD. We apply these
bounds to analyzing the generalization behaviour of linear and two-layer ReLU
networks. Experimental study of these bounds provide some insights on the SGD
training of neural networks. They also point to a new and simple regularization
scheme which we show performs comparably to the current state of the art.

32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an