Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One-pass Stochastic Gradient Descent in Overparametrized Two-layer
  Neural Networks   There has been a recent surge of interest in understanding the convergence of
gradient descent (GD) and stochastic gradient descent (SGD) in
overparameterized neural networks. Most previous works assume that the training
data is provided a priori in a batch, while less attention has been paid to the
important setting where the training data arrives in a stream. In this paper,
we study the streaming data setup and show that with overparamterization and
random initialization, the prediction error of two-layer neural networks under
one-pass SGD converges in expectation. The convergence rate depends on the
eigen-decomposition of the integral operator associated with the so-called
neural tangent kernel (NTK). A key step of our analysis is to show a random
kernel function converges to the NTK with high probability using the VC
dimension and McDiarmid's inequality.

24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex