50,428 research outputs found
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.Comment: 11 pages including referenc
Nonuniform Markov models
A statistical language model assigns probability to strings of arbitrary
length. Unfortunately, it is not possible to gather reliable statistics on
strings of arbitrary length from a finite corpus. Therefore, a statistical
language model must decide that each symbol in a string depends on at most a
small, finite number of other symbols in the string. In this report we propose
a new way to model conditional independence in Markov models. The central
feature of our nonuniform Markov model is that it makes predictions of varying
lengths using contexts of varying lengths. Experiments on the Wall Street
Journal reveal that the nonuniform model performs slightly better than the
classic interpolated Markov model. This result is somewhat remarkable because
both models contain identical numbers of parameters whose values are estimated
in a similar manner. The only difference between the two models is how they
combine the statistics of longer and shorter strings.
Keywords: nonuniform Markov model, interpolated Markov model, conditional
independence, statistical language model, discrete time series.Comment: 17 page
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