Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic Object Classification using CNN ML-MAP layers   Deep networks are currently the state-of-the-art for sensory perception in
autonomous driving and robotics. However, deep models often generate
overconfident predictions precluding proper probabilistic interpretation which
we argue is due to the nature of the SoftMax layer. To reduce the
overconfidence without compromising the classification performance, we
introduce a CNN probabilistic approach based on distributions calculated in the
network's Logit layer. The approach enables Bayesian inference by means of ML
and MAP layers. Experiments with calibrated and the proposed prediction layers
are carried out on object classification using data from the KITTI database.
Results are reported for camera ($RGB$) and LiDAR (range-view) modalities,
where the new approach shows promising performance compared to SoftMax.

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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics