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...
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...
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...
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...
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...