Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
Abstract. An important step in achieving robustness to run-time faults is the ability to detect and repair problems when they arise in a running system. Effective fault detection a...
Paulo Casanova, Bradley R. Schmerl, David Garlan, ...
—This paper proposes LBA, a lifetime balanced data aggregation scheme for asynchronous and duty cycle sensor networks under an application-specific requirement of end-to-end dat...
Active learning aims to reduce the amount of labels required for classification. The main difficulty is to find a good trade-off between exploration and exploitation of the lab...
In recent years, new highly scalable storage systems have significantly contributed to the success of Cloud Computing. Systems like Dynamo or Bigtable have underpinned their abil...
Simon Loesing, Martin Hentschel, Tim Kraska, Donal...