Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize th...
The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been developing ...
Genetic algorithms (GAs) have been applied previously to UML-driven, stress test requirements generation with the aim of increasing chances of discovering faults relating to networ...
Background: The popularity of massively parallel exome and transcriptome sequencing projects demands new data mining tools with a comprehensive set of features to support a wide r...
Classical approaches to shape correspondence base their computation purely on the properties, in particular geometric similarity, of the shapes in question. Their performance stil...
Oliver van Kaick, Andrea Tagliasacchi, Oana Sidi, ...