Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Dual Approach to Scalable Verification of Deep Networks   This paper addresses the problem of formally verifying desirable properties
of neural networks, i.e., obtaining provable guarantees that neural networks
satisfy specifications relating their inputs and outputs (robustness to bounded
norm adversarial perturbations, for example). Most previous work on this topic
was limited in its applicability by the size of the network, network
architecture and the complexity of properties to be verified. In contrast, our
framework applies to a general class of activation functions and specifications
on neural network inputs and outputs. We formulate verification as an
optimization problem (seeking to find the largest violation of the
specification) and solve a Lagrangian relaxation of the optimization problem to
obtain an upper bound on the worst case violation of the specification being
verified. Our approach is anytime i.e. it can be stopped at any time and a
valid bound on the maximum violation can be obtained. We develop specialized
verification algorithms with provable tightness guarantees under special
assumptions and demonstrate the practical significance of our general
verification approach on a variety of verification tasks.

31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga