Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-end Learning of a Convolutional Neural Network via Deep Tensor
  Decomposition   In this paper we study the problem of learning the weights of a deep
convolutional neural network. We consider a network where convolutions are
carried out over non-overlapping patches with a single kernel in each layer. We
develop an algorithm for simultaneously learning all the kernels from the
training data. Our approach dubbed Deep Tensor Decomposition (DeepTD) is based
on a rank-1 tensor decomposition. We theoretically investigate DeepTD under a
realizable model for the training data where the inputs are chosen i.i.d. from
a Gaussian distribution and the labels are generated according to planted
convolutional kernels. We show that DeepTD is data-efficient and provably works
as soon as the sample size exceeds the total number of convolutional weights in
the network. We carry out a variety of numerical experiments to investigate the
effectiveness of DeepTD and verify our theoretical findings.

14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ