Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on th...
Abstract. We consider approaches that allow task migration for scheduling recurrent directed-acyclic-graph (DAG) tasks on symmetric, shared-memory multiprocessors (SMPs) in order t...
In this paper we present a new method of interval fuzzy model identification. The method combines a fuzzy identification methodology with some ideas from linear programming theory...
This paper studies automatic segmentation of multiple
motions from tracked feature points through spectral embedding
and clustering of linear subspaces. We show that
the dimensi...
Abstract-- We present an original graph embedding to speedup distance-range and k-nearest neighbor queries on static and/or dynamic objects located on a (weighted) graph. Our metho...