Learning principle and mathematical realization of the learning mechanism in the brain

While deep learning has achieved remarkable success, there is no clear explanation about why it works so well. In order to discuss this question quantitatively, we need a mathematical framework that explains what learning is in the first place. After several considerations, we succeeded in constructing a mathematical framework that can provide a unified understanding of all types of learning, including deep learning and learning in the brain. We call it learning principle, and it follows that all learning is equivalent to estimating the probability of input data. We not only derived this principle, but also mentioned its application to actual machine learning models. For example, we found that conventional supervised learning is equivalent to estimating conditional probabilities, and succeeded in making supervised learning more effective and generalized. We also proposed a new method of defining the values of estimated probability using differentiation, and showed that unsupervised learning can be performed on arbitrary dataset without any prior knowledge. Namely, this method is a general-purpose machine learning in the true sense. Moreover, we succeeded in describing the learning mechanism in the brain by considering the time evolution of a fully or partially connected model and applying this new method. The learning principle provides solutions to many unsolved problems in deep learning and cognitive neuroscience.Comment: 31 pages, 14 figure

Preface - Advances in Computer Entertainment 10th International Conference, proceedings

These are the proceedings of the 10th International Conference on Advances in Computer Entertainment (ACE 2013), hosted by the Human Media Interaction research group of the Centre for Telematics and Information Technology at the University of Twente, The Netherlands. The ACE series of conferences, held yearly since 2004, has always been lively and interactive events. There are not just mainly paper presentations, but also many creative showcases, demonstrations, workshops, and often a game competition as well. For ten years now, ACE has shown itself to be a strong and vibrant community. Throughout the years, there has been a common element that ties together many of the different types of work presented at ACE. In their contributions, authors not only present solutions to known problems, or observe and describe aspects of the technological reality that is out there, but also actively explore what new things they can make, and why these new things might be important or interestingly different

Rencon Workshop 2011 (Smc-Rencon): Performance Rendering Contest for Computer Systems

(Abstract to follow

CrestMusePEDB 2nd EDITION: MUSIC PERFORMANCE DATABASE WITH PHRASE INFORMATION

(Abstract to follow