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MVA
2007
15 years 7 months ago
Semi-supervised Incremental Learning of Manipulative Tasks
For a social robot, the ability of learning tasks via human demonstration is very crucial. But most current approaches suffer from either the demanding of the huge amount of label...
Zhe Li, Sven Wachsmuth, Jannik Fritsch, Gerhard Sa...
TNN
2008
85views more  TNN 2008»
15 years 6 months ago
Training Spiking Neuronal Networks With Applications in Engineering Tasks
In this paper, spiking neuronal models employing means, variances, and correlations for computation are introduced. We present two approaches in the design of spiking neuronal netw...
Phill Rowcliffe, Jianfeng Feng
PDP
2008
IEEE
16 years 20 days ago
A Transformation Framework for Communicating Multiprocessor-Tasks
Parallel programming models based on a mixture of task and data parallelism have shown to be successful in addressing the increasing communication overhead of distributed memory p...
Jörg Dümmler, Thomas Rauber, Gudula R&uu...
ICML
2010
IEEE
15 years 7 months ago
Bayesian Multi-Task Reinforcement Learning
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Alessandro Lazaric, Mohammad Ghavamzadeh
ECML
2007
Springer
16 years 13 days ago
Learning from Relevant Tasks Only
We extend our recent work on relevant subtask learning, a new variant of multitask learning where the goal is to learn a good classifier for a task-of-interest with too few train...
Samuel Kaski, Jaakko Peltonen