We propose a language-independent approach for improving statistical machine translation for morphologically rich languages using a hybrid morpheme-word representation where the b...
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
We propose a novel hybrid recommendation model in which user preferences and item features are described in terms of semantic concepts defined in domain ontologies. The exploitati...
Reducing power consumption has become a key goal for systemon-a-chip (SOC) designs. Fast and accurate power estimation is needed early in the design process, since power reduction...
When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for large-scale applications suc...
Julia A. Lasserre, Christopher M. Bishop, Thomas P...