We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Supervised clustering is the problem of training a clustering algorithm to produce desirable clusterings: given sets of items and complete clusterings over these sets, we learn ho...
3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac function. It provides for evaluation a mor...
A control part ? data path partition based sequential circuit verification scheme aimed at avoiding state explosion comprises two major modules namely, a data path verifier and a ...