Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Attention Neural Bag-of-Features   In this work, we propose several attention formulations for multivariate
sequence data. We build on top of the recently introduced 2D-Attention and
reformulate the attention learning methodology by quantifying the relevance of
feature/temporal dimensions through latent spaces based on self-attention
rather than learning them directly. In addition, we propose a joint
feature-temporal attention mechanism that learns a joint 2D attention mask
highlighting relevant information without treating feature and temporal
representations independently. The proposed approaches can be used in various
architectures and we specifically evaluate their application together with
Neural Bag of Features feature extraction module. Experiments on several
sequence data analysis tasks show the improved performance yielded by our
approach compared to standard methods.

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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th