Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Connecting Weighted Automata and Recurrent Neural Networks through
  Spectral Learning   In this paper, we unravel a fundamental connection between weighted finite
automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the
case of sequences of discrete symbols, WFAs and 2-RNNs with linear activation
functions are expressively equivalent. Motivated by this result, we build upon
a recent extension of the spectral learning algorithm to vector-valued WFAs and
propose the first provable learning algorithm for linear 2-RNNs defined over
sequences of continuous input vectors. This algorithm relies on estimating low
rank sub-blocks of the so-called Hankel tensor, from which the parameters of a
linear 2-RNN can be provably recovered. The performances of the proposed method
are assessed in a simulation study.

11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph