Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Uncertainty Estimation for Batch Normalized Deep Networks   We show that training a deep network using batch normalization is equivalent
to approximate inference in Bayesian models. We further demonstrate that this
finding allows us to make meaningful estimates of the model uncertainty using
conventional architectures, without modifications to the network or the
training procedure. Our approach is thoroughly validated by measuring the
quality of uncertainty in a series of empirical experiments on different tasks.
It outperforms baselines with strong statistical significance, and displays
competitive performance with recent Bayesian approaches.

14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph