—We present an alternative analysis of weighted 1 minimization for sparse signals with a nonuniform sparsity model, and extend our results to nuclear norm minimization for matric...
We introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent speech data based on a set of state-dependent basis vectors. By incorporating the prior density of sensin...
In this paper, we explore the problem of separating and localizing multiple maneuvering speakers in a reverberant environment. Moreover, the speakers can become silent intermitten...
In this paper we investigate the uniqueness of the 4-way CANDECOMP/PARAFAC (CP) model in the case where the only possible linear dependencies between the columns of the loading ma...
David Brie, Sebastian Miron, Fabrice Caland, Chris...
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...