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...
The placement of regenerators in optical networks has become an active area of research during the last years. Given a set of lightpaths in a network G and a positive integer d, re...
George B. Mertzios, Ignasi Sau, Mordechai Shalom, ...
We present a Bayesian clustering algorithm for multivariate time series. A clustering is regarded as a probabilistic model in which the unknown auto-correlation structure of a tim...
We revisit several maximization problems for geometric networks design under the non-crossing constraint, first studied by Alon, Rajagopalan and Suri (ACM Symposium on Computation...
Abstract. We consider the clustering with diversity problem: given a set of colored points in a metric space, partition them into clusters such that each cluster has at least point...