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» Generalizing over Several Learning Settings
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IEEEPACT
2008
IEEE
14 years 2 months ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribu...
Katherine E. Coons, Behnam Robatmili, Matthew E. T...
CVPR
2010
IEEE
14 years 4 months ago
Unsupervised Learning of Invariant Features Using Video
We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without hu...
David Stavens, Sebastian Thrun
MLG
2007
Springer
14 years 1 months ago
A Universal Kernel for Learning Regular Languages
We give a universal kernel that renders all the regular languages linearly separable. We are not able to compute this kernel efficiently and conjecture that it is intractable, but...
Leonid Kontorovich
AII
1992
13 years 12 months ago
Learning from Multiple Sources of Inaccurate Data
Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies ...
Ganesh Baliga, Sanjay Jain, Arun Sharma
ICML
2005
IEEE
14 years 8 months ago
Coarticulation: an approach for generating concurrent plans in Markov decision processes
We study an approach for performing concurrent activities in Markov decision processes (MDPs) based on the coarticulation framework. We assume that the agent has multiple degrees ...
Khashayar Rohanimanesh, Sridhar Mahadevan