Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Kernelized Capsule Networks   Capsule Networks attempt to represent patterns in images in a way that
preserves hierarchical spatial relationships. Additionally, research has
demonstrated that these techniques may be robust against adversarial
perturbations. We present an improvement to training capsule networks with
added robustness via non-parametric kernel methods. The representations learned
through the capsule network are used to construct covariance kernels for
Gaussian processes (GPs). We demonstrate that this approach achieves comparable
prediction performance to Capsule Networks while improving robustness to
adversarial perturbations and providing a meaningful measure of uncertainty
that may aid in the detection of adversarial inputs.

4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat