10,416 research outputs found
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
Cluster Variation Method in Statistical Physics and Probabilistic Graphical Models
The cluster variation method (CVM) is a hierarchy of approximate variational
techniques for discrete (Ising--like) models in equilibrium statistical
mechanics, improving on the mean--field approximation and the Bethe--Peierls
approximation, which can be regarded as the lowest level of the CVM. In recent
years it has been applied both in statistical physics and to inference and
optimization problems formulated in terms of probabilistic graphical models.
The foundations of the CVM are briefly reviewed, and the relations with
similar techniques are discussed. The main properties of the method are
considered, with emphasis on its exactness for particular models and on its
asymptotic properties.
The problem of the minimization of the variational free energy, which arises
in the CVM, is also addressed, and recent results about both provably
convergent and message-passing algorithms are discussed.Comment: 36 pages, 17 figure
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