AAAI
2012
11 years 6 months ago
2012
The presence of non-symmetric evidence has been a barrier for the application of lifted inference since the evidence destroys the symmetry of the first-order probabilistic model....
AAAI
2012
11 years 6 months ago
2012
Tractable subsets of first-order logic are a central topic in AI research. Several of these formalisms have been used as the basis for first-order probabilistic languages. Howev...
CVPR
2012
IEEE
11 years 10 months ago
2012
IEEE
We present a new algorithm to jointly track multiple objects in multi-view images. While this has been typically addressed separately in the past, we tackle the problem as a single...
CORR
2012
Springer
12 years 6 days ago
2012
Springer
Traditional economic models typically treat private information, or signals, as generated from some underlying state. Recent work has explicated alternative models, where signals ...
ICDAR
2011
IEEE
12 years 4 months ago
2011
IEEE
—Identifying unusual or unique characteristics of an observed sample in useful in forensics in general and handwriting analysis in particular. Rarity is formulated as the probabi...
ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
Graphical models are useful for capturing interdependencies of statistical variables in various fields. Estimating parameters describing sparse graphical models of stationary mul...
TSP
2010
12 years 11 months ago
2010
Graphical models are a framework for representing and exploiting prior conditional independence structures within distributions using graphs. In the Gaussian case, these models are...
TSP
2010
12 years 11 months ago
2010
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
JMLR
2010
12 years 11 months ago
2010
With the increasing popularity of largescale probabilistic graphical models, even "lightweight" approximate inference methods are becoming infeasible. Fortunately, often...
JMLR
2010
12 years 11 months ago
2010
We derive PAC-Bayesian generalization bounds for supervised and unsupervised learning models based on clustering, such as co-clustering, matrix tri-factorization, graphical models...