Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Variational Sufficient Dimensionality Reduction   We consider the problem of sufficient dimensionality reduction (SDR), where
the high-dimensional observation is transformed to a low-dimensional sub-space
in which the information of the observations regarding the label variable is
preserved. We propose DVSDR, a deep variational approach for sufficient
dimensionality reduction. The deep structure in our model has a bottleneck that
represent the low-dimensional embedding of the data. We explain the SDR problem
using graphical models and use the framework of variational autoencoders to
maximize the lower bound of the log-likelihood of the joint distribution of the
observation and label. We show that such a maximization problem can be
interpreted as solving the SDR problem. DVSDR can be easily adopted to
semi-supervised learning setting. In our experiment we show that DVSDR performs
competitively on classification tasks while being able to generate novel data
samples.

36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys