Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Differentially Private Transferrable Deep Learning with
  Membership-Mappings   This paper considers the problem of differentially private semi-supervised
transfer and multi-task learning. The notion of \emph{membership-mapping} has
been developed using measure theory basis to learn data representation via a
fuzzy membership function. An alternative conception of deep autoencoder,
referred to as \emph{Conditionally Deep Membership-Mapping Autoencoder
(CDMMA)}, is considered for transferrable deep learning. Under
practice-oriented settings, an analytical solution for the learning of CDMMA
can be derived by means of variational optimization. The paper proposes a
transfer and multi-task learning approach that combines CDMMA with a tailored
noise adding mechanism to achieve a given level of privacy-loss bound with the
minimum perturbation of the data. Numerous experiments were carried out using
MNIST, USPS, Office, and Caltech256 datasets to verify the competitive robust
performance of the proposed methodology.

7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph