— We study the problem of optimal estimation using quantized innovations, with application to distributed estimation over sensor networks. We show that the state probability dens...
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Abstract. We are interested in the relationship between learning efficiency and representation in the case of supervised neural networks for pattern classification trained by conti...
Model checking has proven to be a useful analysis technique not only for concurrent systems, but also for the genetic regulatory networks (Grns) that govern the functioning of livi...
Radu Mateescu, Pedro T. Monteiro, Estelle Dumas, H...
The present paper reports on an end-to-end application using a deep processing grammar to extract spatial and temporal information of prepositional and adverbial expressions from ...
Lars Hellan, Dorothee Beermann, Jon Atle Gulla, At...