Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks   Can multilayer neural networks -- typically constructed as highly complex
structures with many nonlinearly activated neurons across layers -- behave in a
non-trivial way that yet simplifies away a major part of their complexities? In
this work, we uncover a phenomenon in which the behavior of these complex
networks -- under suitable scalings and stochastic gradient descent dynamics --
becomes independent of the number of neurons as this number grows sufficiently
large. We develop a formalism in which this many-neurons limiting behavior is
captured by a set of equations, thereby exposing a previously unknown operating
regime of these networks. While the current pursuit is mathematically
non-rigorous, it is complemented with several experiments that validate the
existence of this behavior.

35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th