Back to Results
First PageMeta Content
Statistical natural language processing / Deduction / Latent Dirichlet allocation / Expectation–maximization algorithm / Variational message passing / Normal distribution / Mixture model / Entailment / Symbol / Statistics / Mathematics / Logic


On Tight Approximate Inference of the Logistic-Normal Topic Admixture Model Amr Ahmed School of Computer Science Carnegie Mellon University
Add to Reading List

Document Date: 2007-04-20 09:22:03

Open Document

File Size: 2,64 MB

Share Result on Facebook

City

Cambridge / /

Company

Neural Networks / MIT Press / Xing / /

Country

Jordan / /

/

Facility

Computer Science Carnegie Mellon University / /

IndustryTerm

earlier inference algorithm / tight approximate inference algorithm / inference algorithms / extant variational inference algorithms / elegant closed-form variational message passing algorithm / closed form solution / variational inferences algorithms / closedform solution / approximate inference algorithm / generalized mean field algorithm / /

Organization

National Academy of Science / Logistic-Normal Topic Admixture Model Amr Ahmed School of Computer Science Carnegie Mellon University Pittsburgh / Eric P. Xing School of Computer Science Carnegie Mellon University Pittsburgh / MIT / Association for Computational Linguistics / /

Person

David Blei / /

/

Position

model for later reference / correlated topic model / /

ProgrammingLanguage

K / C++ / /

ProvinceOrState

Manitoba / Pennsylvania / /

PublishedMedium

Computational Linguistics / Journal of Machine Learning Research / /

Technology

approximate inference algorithm / earlier inference algorithm / inference algorithms / proposed algorithm / machine translation / variational inferences algorithms / extant variational inference algorithms / elegant closed-form variational message passing algorithm / tight approximate inference algorithm / generalized mean field algorithm / VEM algorithm / Machine Learning / simulation / genotype / /

SocialTag