We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
The approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with different characteristics. One main r...
We study the concept of choice for true concurrency models such as prime event structures and safe Petri nets. We propose a dynamic variation of the notion of cluster previously in...
Wireless sensor networks are proving to be useful in a variety of settings. A core challenge in these networks is to minimize energy consumption. Prior database research has propo...
David Chu, Amol Deshpande, Joseph M. Hellerstein, ...
We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression network, protein complex data, and d...