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IDA
2003
Springer
13 years 9 months ago
Very Predictive Ngrams for Space-Limited Probabilistic Models
In sequential prediction tasks, one repeatedly tries to predict the next element in a sequence. A classical way to solve these problems is to fit an order-n Markov model to the da...
Paul R. Cohen, Charles A. Sutton
ICML
2007
IEEE
14 years 5 months ago
Three new graphical models for statistical language modelling
The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representations to counteract the difficult...
Andriy Mnih, Geoffrey E. Hinton
BMCBI
2011
12 years 11 months ago
N-gram analysis of 970 microbial organisms reveals presence of biological language models
Background: It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as “signature-style” word usage ind...
Hatice U. Osmanbeyoglu, Madhavi Ganapathiraju
TNN
2008
177views more  TNN 2008»
13 years 4 months ago
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting...
Yoshua Bengio, Jean-Sébastien Senecal
NIPS
2000
13 years 5 months ago
A Neural Probabilistic Language Model
A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dim...
Yoshua Bengio, Réjean Ducharme, Pascal Vinc...