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ATAL
2007
Springer
15 years 6 months ago
Model-based function approximation in reinforcement learning
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...
Nicholas K. Jong, Peter Stone
FCCM
1999
IEEE
134views VLSI» more  FCCM 1999»
15 years 4 months ago
Runlength Compression Techniques for FPGA Configurations
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...
Scott Hauck, William D. Wilson
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 3 months ago
Improving evolutionary real-time testing
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...
Marouane Tlili, Stefan Wappler, Harmen Sthamer
NIPS
1994
15 years 1 months ago
Using a neural net to instantiate a deformable model
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...
ECCV
2010
Springer
14 years 12 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
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 ...
Christian Leistner, Amir Saffari, Horst Bischof