Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
We introduce a network-based problem detection framework for distributed systems, which includes a data-mining method for discovering dynamic dependencies among distributed servic...
We present an iterative bootstrapping framework to create and analyze statistical atlases of bony anatomy such as the human pelvis from a large collection of CT data sets. We creat...
Gouthami Chintalapani, Lotta Maria Ellingsen, Ofri...
We consider the Outsourced Aggregation model, where sensing services outsource their sensor data collection and aggregation tasks to third-party service providers called aggregato...
Abstract—Many embedded platforms consist of a heterogeneous collection of processing elements, memory modules, and communication subsystems. These components often implement diff...
Hennadiy Leontyev, Samarjit Chakraborty, James H. ...