Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Deep Structured Models   Many problems in real-world applications involve predicting several random
variables which are statistically related. Markov random fields (MRFs) are a
great mathematical tool to encode such relationships. The goal of this paper is
to combine MRFs with deep learning algorithms to estimate complex
representations while taking into account the dependencies between the output
random variables. Towards this goal, we propose a training algorithm that is
able to learn structured models jointly with deep features that form the MRF
potentials. Our approach is efficient as it blends learning and inference and
makes use of GPU acceleration. We demonstrate the effectiveness of our
algorithm in the tasks of predicting words from noisy images, as well as
multi-class classification of Flickr photographs. We show that joint learning
of the deep features and the MRF parameters results in significant performance
gains.

25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an