Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are b...
Eric H. Huang, Richard Socher, Christopher D. Mann...
In the context of information system engineering, we propose a four-layer metamodeling architecture with a comprehensive set of operations on metamodels. Our architecture enables ...
MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. This enabled prior beliefs about the scale, orientat...
In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented. The proposed method constitutes an extension of the key pa...
Over the past decade, researchers have found context-sensitive term-rewriting semantics to be powerful and expressive tools for modeling programming languages, particularly in esta...
Jacob Matthews, Robert Bruce Findler, Matthew Flat...