Two major goals in machine learning are the discovery of complex multidimensional solutions and continual improvement of existing solutions. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We

. knobs). We introduce the algorithm “Simultaneous On-line Discovery and Improvement of Robotic Skills ” (SODIRS) that is able to autonomously discover and optimize skill options for such task variations. We formalize the problem in a reinforcement learning context, and use the PIBB algorithm [2

Abstract. Strangeness flavor yield s and the entropy yield S are the observables of the deconfined quark-gluon state of matter which can be studied in the entire available experimental energy range at AGS, SPS, RHIC, and, in near future, at the LHC energy range. We present here a comprehensive analysis of strange, soft hadron production as function of energy and reaction volume. We discuss the physical properties of the final state and argue how evidence about the primordial QGP emerges. 1.

researchers from across multiple disciplines to further develop the "thinkingdoing" link in entrepreneurship research. It is our goal to offer colleagues an effective research staging point from which they may embark upon many additional research expeditions and investigations involving

characteristics were so widely known. Nevertheless, this was a very lively issue until quite recently, and it spawned numerous experiments which purported to demonstrate that animals known to have different genotypes also had different scores on a particular learning task. Even today such experiments continue

. Different from conventional attribute discovery meth-ods, our approach does not rely on any pre-defined vocabu-laries and human labeling. Therefore, we are able to build a large visual knowledge base without any human efforts. The discovery is based on a novel deep architecture, named In-dependent Component

Abstract Deep neural networks are being widely used for feature representation learning in diverse problem areas ranging from object recognition and speech recognition to robotic perception and human disease prediction. We demonstrate a novel, perhaps the first application of deep learning

Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university

of the students was to build a non-runnable descriptive model. This meant that they had to abstract away from the rich detail of the learning materials to "reveal" the bare bones of the underlying evolutionary processes. Learning support for 3 this activity was provided by the interactions

First, I would like to thank my advisors Prof. Gal A. Kaminka and Dr. David Sarne for their excellent guidance and constant support in the past two years. I learned quite a bit about scientific research, all thanks to their great instruction. Working with them was a real pleasure, not only