Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Wasserstein Dropout   Despite of its importance for safe machine learning, uncertainty
quantification for neural networks is far from being solved. State-of-the-art
approaches to estimate neural uncertainties are often hybrid, combining
parametric models with explicit or implicit (dropout-based) ensembling. We take
another pathway and propose a novel approach to uncertainty quantification for
regression tasks, Wasserstein dropout, that is purely non-parametric.
Technically, it captures aleatoric uncertainty by means of dropout-based
sub-network distributions. This is accomplished by a new objective which
minimizes the Wasserstein distance between the label distribution and the model
distribution. An extensive empirical analysis shows that Wasserstein dropout
outperforms state-of-the-art methods, on vanilla test data as well as under
distributional shift, in terms of producing more accurate and stable
uncertainty estimates.

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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
3
The categories of this paper are: physics