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CACM
2011
120views more  CACM 2011»
13 years 2 months ago
The sequence memoizer
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Frank Wood, Jan Gasthaus, Cédric Archambeau...
IPM
2007
99views more  IPM 2007»
13 years 7 months ago
Percent perfect performance (PPP)
An information retrieval performance measure that is interpreted as the percent of perfect performance (PPP) can be used to study the effects of the inclusion of specific documen...
Robert M. Losee
EMNLP
2008
13 years 8 months ago
Phrase Translation Probabilities with ITG Priors and Smoothing as Learning Objective
The conditional phrase translation probabilities constitute the principal components of phrase-based machine translation systems. These probabilities are estimated using a heurist...
Markos Mylonakis, Khalil Sima'an
BC
2007
107views more  BC 2007»
13 years 7 months ago
Decoding spike train ensembles: tracking a moving stimulus
We consider the issue of how to read out the information from nonstationary spike train ensembles. Based on the theory of censored data in statistics, we propose a ‘censored’ m...
Enrico Rossoni, Jianfeng Feng
ML
1998
ACM
139views Machine Learning» more  ML 1998»
13 years 7 months ago
The Hierarchical Hidden Markov Model: Analysis and Applications
We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our m...
Shai Fine, Yoram Singer, Naftali Tishby