Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and i...
As computing systems grow in complexity, the cluster and grid communities require more sophisticated tools to diagnose, debug and analyze such systems. We have developed a toolkit...
Mark K. Gardner, Wu-chun Feng, Michael Broxton, Ad...
Clustering is an important characteristic of most sensor applications. In this paper we define COSMOS, the Cluster-based, heterOgeneouS MOdel for Sensor networks. The model assum...
This paper presents a hybrid shared memory architecture which combines the scalability of a multistage interconnection network with the contention reduction benefits of coherent c...
This paper demonstrates a new method for leveraging unstructured annotations to infer semantic document properties. We consider the domain of product reviews, which are often anno...
S. R. K. Branavan, Harr Chen, Jacob Eisenstein, Re...