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ICML
2005
IEEE
16 years 7 months ago
Bayesian sparse sampling for on-line reward optimization
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
ML
2010
ACM
155views Machine Learning» more  ML 2010»
15 years 5 months ago
On the infeasibility of modeling polymorphic shellcode - Re-thinking the role of learning in intrusion detection systems
Current trends demonstrate an increasing use of polymorphism by attackers to disguise their exploits. The ability for malicious code to be easily, and automatically, transformed in...
Yingbo Song, Michael E. Locasto, Angelos Stavrou, ...
ICMLA
2008
15 years 8 months ago
Multi-stage Learning of Linear Algebra Algorithms
In evolving applications, there is a need for the dynamic selection of algorithms or algorithm parameters. Such selection is hardly ever governed by exact theory, so intelligent r...
Victor Eijkhout, Erika Fuentes
IROS
2006
IEEE
126views Robotics» more  IROS 2006»
16 years 17 days ago
A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks
Abstract— Tying suture knots is a time-consuming task performed frequently during Minimally Invasive Surgery (MIS). Automating this task could greatly reduce total surgery time f...
Hermann Georg Mayer, Faustino J. Gomez, Daan Wiers...
ICCS
2007
Springer
16 years 21 days ago
Towards Real-Time Distributed Signal Modeling for Brain-Machine Interfaces
New architectures for Brain-Machine Interface communication and control use mixture models for expanding rehabilitation capabilities of disabled patients. Here we present and test ...
Jack DiGiovanna, Loris Marchal, Prapaporn Rattanat...