Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-Quality Prediction Intervals for Deep Learning: A
  Distribution-Free, Ensembled Approach   This paper considers the generation of prediction intervals (PIs) by neural
networks for quantifying uncertainty in regression tasks. It is axiomatic that
high-quality PIs should be as narrow as possible, whilst capturing a specified
portion of data. We derive a loss function directly from this axiom that
requires no distributional assumption. We show how its form derives from a
likelihood principle, that it can be used with gradient descent, and that model
uncertainty is accounted for in ensembled form. Benchmark experiments show the
method outperforms current state-of-the-art uncertainty quantification methods,
reducing average PI width by over 10%.

2
The categories of this paper are: math
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
1
The categories of this paper are: cs
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom