Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Two-layer Neural Networks with Symmetric Inputs   We give a new algorithm for learning a two-layer neural network under a
general class of input distributions. Assuming there is a ground-truth
two-layer network $$ y = A \sigma(Wx) + \xi, $$ where $A,W$ are weight
matrices, $\xi$ represents noise, and the number of neurons in the hidden layer
is no larger than the input or output, our algorithm is guaranteed to recover
the parameters $A,W$ of the ground-truth network. The only requirement on the
input $x$ is that it is symmetric, which still allows highly complicated and
structured input.
  Our algorithm is based on the method-of-moments framework and extends several
results in tensor decompositions. We use spectral algorithms to avoid the
complicated non-convex optimization in learning neural networks. Experiments
show that our algorithm can robustly learn the ground-truth neural network with
a small number of samples for many symmetric input distributions.

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
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas