This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test o...
Networked embedded acoustic sensors and imagers allow scientists to observe biological and environmental phenomena at high sampling rates and multiple scales. Such sampling can cr...
Michael Allen, Eric Graham, Shaun Ahmadian, Tetsun...
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
Abstract. Reliably extracting information from aerial imagery is a difficult problem with many practical applications. One specific case of this problem is the task of automatica...
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density func...