Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Generalized Canonical Correlation Analysis   We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a
method for learning nonlinear transformations of arbitrarily many views of
data, such that the resulting transformations are maximally informative of each
other. While methods for nonlinear two-view representation learning (Deep CCA,
(Andrew et al., 2013)) and linear many-view representation learning
(Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview
representation learning technique that combines the flexibility of nonlinear
(deep) representation learning with the statistical power of incorporating
information from many independent sources, or views. We present the DGCCA
formulation as well as an efficient stochastic optimization algorithm for
solving it. We learn DGCCA representations on two distinct datasets for three
downstream tasks: phonetic transcription from acoustic and articulatory
measurements, and recommending hashtags and friends on a dataset of Twitter
users. We find that DGCCA representations soundly beat existing methods at
phonetic transcription and hashtag recommendation, and in general perform no
worse than standard linear many-view techniques.

18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph