Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Machine Learning Phases of Matter   We study the robustness of machine learning approaches to adversarial
perturbations, with a focus on supervised learning scenarios. We find that
typical phase classifiers based on deep neural networks are extremely
vulnerable to adversarial perturbations: adding a tiny amount of carefully
crafted noises into the original legitimate examples will cause the classifiers
to make incorrect predictions at a notably high confidence level. Through the
lens of activation maps, we find that some important underlying physical
principles and symmetries remain to be adequately captured for classifiers with
even near-perfect performance. This explains why adversarial perturbations
exist for fooling these classifiers. In addition, we find that, after
adversarial training, the classifiers will become more consistent with physical
laws and consequently more robust to certain kinds of adversarial
perturbations. Our results provide valuable guidance for both theoretical and
experimental future studies on applying machine learning techniques to
condensed matter physics.

19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex