Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Defense Through Diverse Directions   In this work we develop a novel Bayesian neural network methodology to
achieve strong adversarial robustness without the need for online adversarial
training. Unlike previous efforts in this direction, we do not rely solely on
the stochasticity of network weights by minimizing the divergence between the
learned parameter distribution and a prior. Instead, we additionally require
that the model maintain some expected uncertainty with respect to all input
covariates. We demonstrate that by encouraging the network to distribute evenly
across inputs, the network becomes less susceptible to localized, brittle
features which imparts a natural robustness to targeted perturbations. We show
empirical robustness on several benchmark datasets.

4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
29
The categories of this paper are: funct-an