Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Density estimation in representation space to predict model uncertainty   Deep learning models frequently make incorrect predictions with high
confidence when presented with test examples that are not well represented in
their training dataset. We propose a novel and straightforward approach to
estimate prediction uncertainty in a pre-trained neural network model. Our
method estimates the training data density in representation space for a novel
input. A neural network model then uses this information to determine whether
we expect the pre-trained model to make a correct prediction. This uncertainty
model is trained by predicting in-distribution errors, but can detect
out-of-distribution data without having seen any such example. We test our
method for a state-of-the art image classification model in the settings of
both in-distribution uncertainty estimation as well as out-of-distribution
detection.

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The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th