Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ridge Regression with Over-Parametrized Two-Layer Networks Converge to
  Ridgelet Spectrum   Characterization of local minima draws much attention in theoretical studies
of deep learning. In this study, we investigate the distribution of parameters
in an over-parametrized finite neural network trained by ridge regularized
empirical square risk minimization (RERM). We develop a new theory of ridgelet
transform, a wavelet-like integral transform that provides a powerful and
general framework for the theoretical study of neural networks involving not
only the ReLU but general activation functions. We show that the distribution
of the parameters converges to a spectrum of the ridgelet transform. This
result provides a new insight into the characterization of the local minima of
neural networks, and the theoretical background of an inductive bias theory
based on lazy regimes. We confirm the visual resemblance between the parameter
distribution trained by SGD, and the ridgelet spectrum calculated by numerical
integration through numerical experiments with finite models.

3
The categories of this paper are: physics
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The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
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The categories of this paper are: cond-mat