Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty-Aware Attention for Reliable Interpretation and Prediction   Attention mechanism is effective in both focusing the deep learning models on
relevant features and interpreting them. However, attentions may be unreliable
since the networks that generate them are often trained in a weakly-supervised
manner. To overcome this limitation, we introduce the notion of input-dependent
uncertainty to the attention mechanism, such that it generates attention for
each feature with varying degrees of noise based on the given input, to learn
larger variance on instances it is uncertain about. We learn this
Uncertainty-aware Attention (UA) mechanism using variational inference, and
validate it on various risk prediction tasks from electronic health records on
which our model significantly outperforms existing attention models. The
analysis of the learned attentions shows that our model generates attentions
that comply with clinicians' interpretation, and provide richer interpretation
via learned variance. Further evaluation of both the accuracy of the
uncertainty calibration and the prediction performance with "I don't know"
decision show that UA yields networks with high reliability as well.

14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc