Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lipschitz-Margin Training: Scalable Certification of Perturbation
  Invariance for Deep Neural Networks   High sensitivity of neural networks against malicious perturbations on inputs
causes security concerns. To take a steady step towards robust classifiers, we
aim to create neural network models provably defended from perturbations. Prior
certification work requires strong assumptions on network structures and
massive computational costs, and thus the range of their applications was
limited. From the relationship between the Lipschitz constants and prediction
margins, we present a computationally efficient calculation technique to
lower-bound the size of adversarial perturbations that can deceive networks,
and that is widely applicable to various complicated networks. Moreover, we
propose an efficient training procedure that robustifies networks and
significantly improves the provably guarded areas around data points. In
experimental evaluations, our method showed its ability to provide a
non-trivial guarantee and enhance robustness for even large networks.

25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph