Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluating the Robustness of Bayesian Neural Networks Against Different
  Types of Attacks   To evaluate the robustness gain of Bayesian neural networks on image
classification tasks, we perform input perturbations, and adversarial attacks
to the state-of-the-art Bayesian neural networks, with a benchmark CNN model as
reference. The attacks are selected to simulate signal interference and
cyberattacks towards CNN-based machine learning systems. The result shows that
a Bayesian neural network achieves significantly higher robustness against
adversarial attacks generated against a deterministic neural network model,
without adversarial training. The Bayesian posterior can act as the safety
precursor of ongoing malicious activities. Furthermore, we show that the
stochastic classifier after the deterministic CNN extractor has sufficient
robustness enhancement rather than a stochastic feature extractor before the
stochastic classifier. This advises on utilizing stochastic layers in building
decision-making pipelines within a safety-critical domain.

3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys