Compressed sensing (CS), a joint compression and sensing process, is a emerging field of activity in which the signal is sampled and simultaneously compressed at a greatly reduced...
Vo Dinh Minh Nhat, Duc Vo, Subhash Challa, Sungyou...
We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of t...
In this work, a new algorithm is proposed for fast estimation of nonparametric multivariate kernel density, based on principal direction divisive partitioning (PDDP) of the data s...
Constructing a high-resolution (HR) image from lowresolution (LR) image(s) has been a very active research topic recently with focus shifting from multi-frames to learning based s...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...