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Many classes of objects can now be successfully detected with statistical machine learning techniques. Faces, cars and pedestrians, have all been detected with low error rates by learning their appearance in a highly generic manner from extensive training ...
We investigated if visual and visuomotor perceptual learning rely on the same underlying mechanisms. Observers trained with line bisection stimuli in which a center line was either closer to a right or left outer line. In the visuomotor learning condition, ...
Technology is playing an increasing role in our society. Therefore it becomes important to educate the general public, and young generations in particular, about the most common technologies. In this context, robots are excellent education tools, for many ...
Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In th ...
We study the task of learning to rank images given a text query, a problem that is complicated by the issue of multiple senses. That is, the senses of interest are typically the visually distinct concepts that a user wishes to retrieve. In this paper, we p ...
Detecting errors in other's actions is of pivotal importance for joint action, competitive behavior and observational learning. Although many studies have focused on the neural mechanisms involved in detecting low-level errors, relatively little is known a ...
Teaching competence is an important skill for graduate students to acquire and is often considered a precursor to an academic career. In this study, we evaluated the effects of a multi-day teaching workshop on graduate teaching philosophies by surveying 20 ...
While it is well established that stress can modulate declarative learning, very few studies have investigated the influence of stress on non-declarative learning. Here, we studied the influence of post-learning stress, which effectively modulates declarat ...
In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according t ...
We examine the problem of learning a set of parameters from a distributed dataset. We assume the datasets are collected by agents over a distributed ad-hoc network, and that the communication of the actual raw data is prohibitive due to either privacy cons ...
