Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A framework for benchmarking uncertainty in deep regression   We propose a framework for the assessment of uncertainty quantification in
deep regression. The framework is based on regression problems where the
regression function is a linear combination of nonlinear functions. Basically,
any level of complexity can be realized through the choice of the nonlinear
functions and the dimensionality of their domain. Results of an uncertainty
quantification for deep regression are compared against those obtained by a
statistical reference method. The reference method utilizes knowledge of the
underlying nonlinear functions and is based on a Bayesian linear regression
using a reference prior. Reliability of uncertainty quantification is assessed
in terms of coverage probabilities, and accuracy through the size of calculated
uncertainties. We illustrate the proposed framework by applying it to current
approaches for uncertainty quantification in deep regression. The flexibility,
together with the availability of a reference solution, makes the framework
suitable for defining benchmark sets for uncertainty quantification.

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The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph