Complex networks have been used successfully in scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. Until recently, studies of...
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...
With the arrival of high throughput genotyping techniques, the detection of likely genotyping errors is becoming an increasingly important problem. In this paper we are interested...
The efficient management of complex objects has become an enabling technology for modern multimedia information systems as well as for many novel database applications. Unfortunat...
Hans-Peter Kriegel, Peter Kunath, Martin Pfeifle, ...