Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Restricted Recurrent Neural Networks   Recurrent Neural Network (RNN) and its variations such as Long Short-Term
Memory (LSTM) and Gated Recurrent Unit (GRU), have become standard building
blocks for learning online data of sequential nature in many research areas,
including natural language processing and speech data analysis. In this paper,
we present a new methodology to significantly reduce the number of parameters
in RNNs while maintaining performance that is comparable or even better than
classical RNNs. The new proposal, referred to as Restricted Recurrent Neural
Network (RRNN), restricts the weight matrices corresponding to the input data
and hidden states at each time step to share a large proportion of parameters.
The new architecture can be regarded as a compression of its classical
counterpart, but it does not require pre-training or sophisticated parameter
fine-tuning, both of which are major issues in most existing compression
techniques. Experiments on natural language modeling show that compared with
its classical counterpart, the restricted recurrent architecture generally
produces comparable results at about 50\% compression rate. In particular, the
Restricted LSTM can outperform classical RNN with even less number of
parameters.

13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
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The categories of this paper are: solv-int
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org