Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence
  Learning   Long Short-Term Memory (LSTM) is a popular approach to boosting the ability
of Recurrent Neural Networks to store longer term temporal information. The
capacity of an LSTM network can be increased by widening and adding layers.
However, usually the former introduces additional parameters, while the latter
increases the runtime. As an alternative we propose the Tensorized LSTM in
which the hidden states are represented by tensors and updated via a
cross-layer convolution. By increasing the tensor size, the network can be
widened efficiently without additional parameters since the parameters are
shared across different locations in the tensor; by delaying the output, the
network can be deepened implicitly with little additional runtime since deep
computations for each timestep are merged into temporal computations of the
sequence. Experiments conducted on five challenging sequence learning tasks
show the potential of the proposed model.

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The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
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The categories of this paper are: econ
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The categories of this paper are: nlin
7
The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: math
8
The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph