In this paper, we tackle the problem of understanding the temporal structure of complex events in highly varying videos obtained from the Internet. Towards this goal, we utilize a...
This paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function...
Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven diffic...
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
—This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by h...
It is probably fair to say that exact inference in graphical models is considered a solved problem, at least regarding its computational complexity: it is exponential in the treew...
Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an ...
—In this paper, we investigate data parallelism in exact inference with respect to arbitrary junction trees. Exact inference is a key problem in exploring probabilistic graphical...
—We present the design and implementation of a parallel exact inference algorithm on the Cell Broadband Engine (Cell BE). Exact inference is a key problem in exploring probabilis...
—A recent paper [1] proposed a provably optimal polynomial time method for performing near-isometric point pattern matching by means of exact probabilistic inference in a chordal...