Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Uncertainty with Artificial Neural Networks for Improved
  Remaining Time Prediction of Business Processes   Artificial neural networks will always make a prediction, even when
completely uncertain and regardless of the consequences. This obliviousness of
uncertainty is a major obstacle towards their adoption in practice. Techniques
exist, however, to estimate the two major types of uncertainty: model
uncertainty and observation noise in the data. Bayesian neural networks are
theoretically well-founded models that can learn the model uncertainty of their
predictions. Minor modifications to these models and their loss functions allow
learning the observation noise for individual samples as well. This paper is
the first to apply these techniques to predictive process monitoring. We found
that they contribute towards more accurate predictions and work quickly.
However, their main benefit resides with the uncertainty estimates themselves
that allow the separation of higher-quality from lower-quality predictions and
the building of confidence intervals. This leads to many interesting
applications, enables an earlier adoption of prediction systems with smaller
datasets and fosters a better cooperation with humans.

23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga