Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Generalization Bounds of a Family of Recurrent Neural Networks   Recurrent Neural Networks (RNNs) have been widely applied to sequential data
analysis. Due to their complicated modeling structures, however, the theory
behind is still largely missing. To connect theory and practice, we study the
generalization properties of vanilla RNNs as well as their variants, including
Minimal Gated Unit (MGU), Long Short Term Memory (LSTM), and Convolutional
(Conv) RNNs. Specifically, our theory is established under the PAC-Learning
framework. The generalization bound is presented in terms of the spectral norms
of the weight matrices and the total number of parameters. We also establish
refined generalization bounds with additional norm assumptions, and draw a
comparison among these bounds. We remark: (1) Our generalization bound for
vanilla RNNs is significantly tighter than the best of existing results; (2) We
are not aware of any other generalization bounds for MGU, LSTM, and Conv RNNs
in the exiting literature; (3) We demonstrate the advantages of these variants
in generalization.

34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat