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NIPS
2007
13 years 8 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
JMLR
2011
187views more  JMLR 2011»
13 years 2 months ago
Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation
We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to learn the grammatical rules and context dependent changes using ...
Yizhao Ni, Craig Saunders, Sándor Szedm&aac...
EMNLP
2010
13 years 5 months ago
Using Universal Linguistic Knowledge to Guide Grammar Induction
We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-...
Tahira Naseem, Harr Chen, Regina Barzilay, Mark Jo...
ACL
1998
13 years 8 months ago
Automatic Acquisition of Language Model based on Head-Dependent Relation between Words
Language modeling is to associate a sequence of words with a priori probability, which is a key part of many natural language applications such as speech recognition and statistic...
Seungmi Lee, Key-Sun Choi
ICML
2007
IEEE
14 years 8 months ago
Modeling changing dependency structure in multivariate time series
We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations usi...
Xiang Xuan, Kevin P. Murphy