Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
The time it takes to reconfigure FPGAs can be a significant overhead for reconfigurable computing. In this paper we develop new compression algorithms for FPGA configurations that...
Embedded systems are often used in a safety-critical context, e.g. in airborne or vehicle systems. Typically, timing constraints must be satisfied so that real-time embedded syste...
Deformable models are an attractive approach to recognizing nonrigid objects which have considerable within class variability. However, there are severe search problems associated...
Christopher K. I. Williams, Michael Revow, Geoffre...
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...