It is challenging to test applications and functions for which the correct output for arbitrary input cannot be known in advance, e.g. some computational science or machine learni...
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
We present a theoretical analysis of supervised ranking, providing necessary and sufficient conditions for the asymptotic consistency of algorithms based on minimizing a surrogate...
e a new operating system abstraction for partitioning systems, allowing multiple applications to run in isolation from each other on the same physical hardware. This isolation prev...
Executable UML allows precisely describing the softtem at a higher level of abstraction. It bridges the semantics gap between the UML design models and the implementation. The exe...