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...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
We consider the problem of recursively and causally reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of noisy...
This research investigates distributed clustering scheme and proposes a cluster-based routing protocol for DelayTolerant Mobile Networks (DTMNs). The basic idea is to distributivel...
Electro-Magnetic Analysis has been identified as an efficient technique to retrieve the secret key of cryptographic algorithms. Although similar mathematically speaking, Power or E...
The basic idea of an algebraic approach to learning Bayesian network (BN) structures is to represent every BN structure by a certain uniquely determined vector, called the standar...