Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A self consistent theory of Gaussian Processes captures feature learning
  effects in finite CNNs   Deep neural networks (DNNs) in the infinite width/channel limit have received
much attention recently, as they provide a clear analytical window to deep
learning via mappings to Gaussian Processes (GPs). Despite its theoretical
appeal, this viewpoint lacks a crucial ingredient of deep learning in finite
DNNs, laying at the heart of their success -- feature learning. Here we
consider DNNs trained with noisy gradient descent on a large training set and
derive a self consistent Gaussian Process theory accounting for strong
finite-DNN and feature learning effects. Applying this to a toy model of a
two-layer linear convolutional neural network (CNN) shows good agreement with
experiments. We further identify, both analytical and numerically, a sharp
transition between a feature learning regime and a lazy learning regime in this
model. Strong finite-DNN effects are also derived for a non-linear two-layer
fully connected network. Our self consistent theory provides a rich and
versatile analytical framework for studying feature learning and other non-lazy
effects in finite DNNs.

23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th