Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Sparsification of Gated Recurrent Neural Networks   Bayesian methods have been successfully applied to sparsify weights of neural
networks and to remove structure units from the networks, e. g. neurons. We
apply and further develop this approach for gated recurrent architectures.
Specifically, in addition to sparsification of individual weights and neurons,
we propose to sparsify preactivations of gates and information flow in LSTM. It
makes some gates and information flow components constant, speeds up forward
pass and improves compression. Moreover, the resulting structure of gate
sparsity is interpretable and depends on the task. Code is available on github:
https://github.com/tipt0p/SparseBayesianRNN

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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg