We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudiment...
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
Building robots can be a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to cope the...
Abstract. We are interested in the relationship between learning efficiency and representation in the case of supervised neural networks for pattern classification trained by conti...
Transfer learning is the ability of an agent to apply knowledge learned in previous tasks to new problems or domains. We approach this problem by focusing on model formulation, i....