Ensemble learning is a variational Bayesian method in which an intractable distribution is approximated by a lower-bound. Ensemble learning results in models with better generaliz...
A file data model for algorithmic skeletons is proposed, focusing on transparency and efficiency. Algorithmic skeletons correspond to a high-level programming model that takes a...
To maintain consistency, designers of replicated services have traditionally been forced to choose from either strong consistency guarantees or none at all. Realizing that a conti...
Accurate network measurement through trace collection is critical for advancing network design and for maintaining secure, reliable networks. Unfortunately, the release of network...
Bruno F. Ribeiro, Weifeng Chen, Gerome Miklau, Don...
Truly ubiquitous computing poses new and significant challenges. A huge number of heterogeneous devices will interact to perform complex distributed tasks. One of the key aspects...
Nicola Bicocchi, Marco Mamei, Andrea Prati, Rita C...