This paper addresses the problem of learning to map sentences to logical form, given training data consisting of natural language sentences paired with logical representations of ...
Tom Kwiatkowksi, Luke S. Zettlemoyer, Sharon Goldw...
— A paramount challenge in probabilistic databases is the scalable computation of confidences of tuples in query results. This paper introduces an efficient secondary-storage o...
We introduce a mixture of probabilistic canonical correlation analyzers model for analyzing local correlations, or more generally mutual statistical dependencies, in cooccurring d...
This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize w...
As part of the European PROLIX (Process Oriented Learning and Information eXchange) project, VUB STARLab designed a generic ontologybased data matching framework (ODMF). Within the...