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We propose an adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes. The cost function is assumed to consist of a collection of individual components. Diffusion adaptation allows the nodes to coopera ...
In the world of High Performance Computing (newly renamed "High Productivity Computing"), where the race for performance is on, where the hunt for the last Flop rages and where the developers swear by the Cult of Power, the different applications have diff ...
While stress has been shown to affect higher-level cognitive learning (such as learning to remember pictures of different emotional salience), it does not affect perceptual learning. ...
This paper introduces a computer-based training program for enhancing numerical cognition aimed at children with developmental dyscalculia. Through modelling cognitive processes and controlling the level of their stimulation, the system optimizes the learn ...
Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscienc ...
Tangible user interfaces (TUIs) have been the focus of much attention recently in the HCI and learning communities. Although TUIs seem to intuitively offer potential to increase the learning experience, there have been questions about whether they actually ...
The canonical Robot Learning from Demonstration scenario has a robot observing human demonstrations of a task or behavior in a few situations, and then developing a generalized controller. Current work further refines the learned system, often to perform th ...
Perceptual learning is the ability to improve perception through practice. Perceptual learning is usually specific for the task and features learned. For example, improvements in performance for a certain stimulus do not transfer if the stimulus is rotated ...
The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning (MKL) enables to learn the kernel, from an ensemble of ...
The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning (MKL) enables to learn the kernel, from an ensemble of ...
