There has been recent interest in the problem of decoding letter substitution ciphers using techniques inspired by natural language processing. We consider a different type of cla...
This paper addresses the discovery of activities and learns the underlying processes that govern their occurrences over time in complex surveillance scenes. To this end, we propos...
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomp...
Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
We present a corpus{based approach to word{sense disambiguation that only requires information that can be automatically extracted from untagged text. We use unsupervised techniqu...
Gibbs sampling also known as Glauber dynamics is a popular technique for sampling high dimensional distributions defined on graphs. Of special interest is the behavior of Gibbs sa...
We provide provably privacy-preserving versions of belief propagation, Gibbs sampling, and other local algorithms — distributed multiparty protocols in which each party or verte...
Abstract. There is currently a large interest in probabilistic logical models. A popular algorithm for approximate probabilistic inference with such models is Gibbs sampling. From ...
— We investigate the problem of inferring the packet loss characteristics of Internet links using server-based measurements. Unlike much of existing work on network tomography th...