Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Clockwork RNN   Sequence prediction and classification are ubiquitous and challenging
problems in machine learning that can require identifying complex dependencies
between temporally distant inputs. Recurrent Neural Networks (RNNs) have the
ability, in theory, to cope with these temporal dependencies by virtue of the
short-term memory implemented by their recurrent (feedback) connections.
However, in practice they are difficult to train successfully when the
long-term memory is required. This paper introduces a simple, yet powerful
modification to the standard RNN architecture, the Clockwork RNN (CW-RNN), in
which the hidden layer is partitioned into separate modules, each processing
inputs at its own temporal granularity, making computations only at its
prescribed clock rate. Rather than making the standard RNN models more complex,
CW-RNN reduces the number of RNN parameters, improves the performance
significantly in the tasks tested, and speeds up the network evaluation. The
network is demonstrated in preliminary experiments involving two tasks: audio
signal generation and TIMIT spoken word classification, where it outperforms
both RNN and LSTM networks.

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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci