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NIPS
1998
15 years 4 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
111
Voted
GECCO
2007
Springer
212views Optimization» more  GECCO 2007»
15 years 8 months ago
A developmental model of neural computation using cartesian genetic programming
The brain has long been seen as a powerful analogy from which novel computational techniques could be devised. However, most artificial neural network approaches have ignored the...
Gul Muhammad Khan, Julian F. Miller, David M. Hall...
ECCV
2006
Springer
16 years 4 months ago
Globally Optimal Active Contours, Sequential Monte Carlo and On-Line Learning for Vessel Segmentation
In this paper we propose a Particle Filter-based propagation approach for the segmentation of vascular structures in 3D volumes. Because of pathologies and inhomogeneities, many de...
Charles Florin, Nikos Paragios, James Williams
130
Voted
HYBRID
2003
Springer
15 years 7 months ago
Mode Reconstruction for Source Coding and Multi-modal Control
s of Invited Presentations The Mathematics of Matter and the Mathematics of Mind . . . . . . . . . . . . . 1 David Berlinski A Grand Challenge: Full Reactive Modeling of a Multi-ce...
Adam Austin, Magnus Egerstedt
CIA
2007
Springer
15 years 8 months ago
Quantifying the Expected Utility of Information in Multi-agent Scheduling Tasks
Abstract. In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complet...
Avi Rosenfeld, Sarit Kraus, Charlie Ortiz