Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simple and Principled Uncertainty Estimation with Deterministic Deep
  Learning via Distance Awareness   Bayesian neural networks (BNN) and deep ensembles are principled approaches
to estimate the predictive uncertainty of a deep learning model. However their
practicality in real-time, industrial-scale applications are limited due to
their heavy memory and inference cost. This motivates us to study principled
approaches to high-quality uncertainty estimation that require only a single
deep neural network (DNN). By formalizing the uncertainty quantification as a
minimax learning problem, we first identify input distance awareness, i.e., the
model's ability to quantify the distance of a testing example from the training
data in the input space, as a necessary condition for a DNN to achieve
high-quality (i.e., minimax optimal) uncertainty estimation. We then propose
Spectral-normalized Neural Gaussian Process (SNGP), a simple method that
improves the distance-awareness ability of modern DNNs, by adding a weight
normalization step during training and replacing the output layer with a
Gaussian process. On a suite of vision and language understanding tasks and on
modern architectures (Wide-ResNet and BERT), SNGP is competitive with deep
ensembles in prediction, calibration and out-of-domain detection, and
outperforms the other single-model approaches.

18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat