We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In an earlier paper, we introduced a class of...
Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of tra...
Time triggered methods provide deterministic behaviour suitable for critical real-time systems. They perform less favourably, however, if the arrival times of some activities are ...
Real programming languages are often defined using ambiguous context-free grammars. Some ambiguity is intentional while other ambiguity is accidental. A good grammar development e...