Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unimodal probability distributions for deep ordinal classification   Probability distributions produced by the cross-entropy loss for ordinal
classification problems can possess undesired properties. We propose a
straightforward technique to constrain discrete ordinal probability
distributions to be unimodal via the use of the Poisson and binomial
probability distributions. We evaluate this approach in the context of deep
learning on two large ordinal image datasets, obtaining promising results.

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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: econ
10
The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys