Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning on Attributed Sequences   Recent research in feature learning has been extended to sequence data, where
each instance consists of a sequence of heterogeneous items with a variable
length. However, in many real-world applications, the data exists in the form
of attributed sequences, which is composed of a set of fixed-size attributes
and variable-length sequences with dependencies between them. In the attributed
sequence context, feature learning remains challenging due to the dependencies
between sequences and their associated attributes. In this dissertation, we
focus on analyzing and building deep learning models for four new problems on
attributed sequences. Our extensive experiments on real-world datasets
demonstrate that the proposed solutions significantly improve the performance
of each task over the state-of-the-art methods on attributed sequences.

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The categories of this paper are: math
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The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci