Abstract. Services are subject to constant change and variation. Services can evolve typically due to changes in structure, e.g., attributes and operations; in behavior and policie...
Changes in the underpinning technologies for TEL is occurring at a pace that we have never before experienced, and this is unlikely to slow down. This necessitates a broader and mo...
Andrew Ravenscroft, Tom Boyle, John Cook, Andreas ...
We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity...
The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
We show how nonlinear embedding algorithms popular for use with shallow semisupervised learning techniques such as kernel methods can be applied to deep multilayer architectures, ...