Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Verifiability and Predictability: Interpreting Utilities of Network
  Architectures for Point Cloud Processing   In this paper, we diagnose deep neural networks for 3D point cloud processing
to explore utilities of different intermediate-layer network architectures. We
propose a number of hypotheses on the effects of specific intermediate-layer
network architectures on the representation capacity of DNNs. In order to prove
the hypotheses, we design five metrics to diagnose various types of DNNs from
the following perspectives, information discarding, information concentration,
rotation robustness, adversarial robustness, and neighborhood inconsistency. We
conduct comparative studies based on such metrics to verify the hypotheses. We
further use the verified hypotheses to revise intermediate-layer architectures
of existing DNNs and improve their utilities. Experiments demonstrate the
effectiveness of our method.

24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph