Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Calibration of Model Uncertainty for Dropout Variational Inference   The model uncertainty obtained by variational Bayesian inference with Monte
Carlo dropout is prone to miscalibration. In this paper, different logit
scaling methods are extended to dropout variational inference to recalibrate
model uncertainty. Expected uncertainty calibration error (UCE) is presented as
a metric to measure miscalibration. The effectiveness of recalibration is
evaluated on CIFAR-10/100 and SVHN for recent CNN architectures. Experimental
results show that logit scaling considerably reduce miscalibration by means of
UCE. Well-calibrated uncertainty enables reliable rejection of uncertain
predictions and robust detection of out-of-distribution data.

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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
29
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: q-fin
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The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: eess