Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Training and Provable Robustness: A Tale of Two Objectives   We propose a principled framework that combines adversarial training and
provable robustness verification for training certifiably robust neural
networks. We formulate the training problem as a joint optimization problem
with both empirical and provable robustness objectives and develop a novel
gradient-descent technique that can eliminate bias in stochastic
multi-gradients. We perform both theoretical analysis on the convergence of the
proposed technique and experimental comparison with state-of-the-arts. Results
on MNIST and CIFAR-10 show that our method can consistently match or outperform
prior approaches for provable l infinity robustness. Notably, we achieve 6.60%
verified test error on MNIST at epsilon = 0.3, and 66.57% on CIFAR-10 with
epsilon = 8/255.

25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci