Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An analytic theory of shallow networks dynamics for hinge loss
  classification   Neural networks have been shown to perform incredibly well in classification
tasks over structured high-dimensional datasets. However, the learning dynamics
of such networks is still poorly understood. In this paper we study in detail
the training dynamics of a simple type of neural network: a single hidden layer
trained to perform a classification task. We show that in a suitable mean-field
limit this case maps to a single-node learning problem with a time-dependent
dataset determined self-consistently from the average nodes population. We
specialize our theory to the prototypical case of a linearly separable dataset
and a linear hinge loss, for which the dynamics can be explicitly solved. This
allow us to address in a simple setting several phenomena appearing in modern
networks such as slowing down of training dynamics, crossover between rich and
lazy learning, and overfitting. Finally, we asses the limitations of mean-field
theory by studying the case of large but finite number of nodes and of training
samples.

30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ