Deep-layer machine learning architectures continue to emerge as a promising biologically-inspired framework for achieving scalable perception in artificial agents. State inference ...
The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representations to counteract the difficult...
Probabilistic models of languages are fundamental to understand and learn the profile of the subjacent code in order to estimate its entropy, enabling the verification and predicti...
With the recent advances in massively parallel programmable processor networks, methods for the infusion of massive MIMD parallelism into programs have become increasingly relevant...
The rapid growth of the Internet over the last decade has been startling. However, efforts to track its growth have often fallen afoul of bad data -- for instance, how much traffi...