H
Hagai Attias
Researcher at Microsoft
Publications - 90
Citations - 5222
Hagai Attias is an academic researcher from Microsoft. The author has contributed to research in topics: Graphical model & Expectation–maximization algorithm. The author has an hindex of 30, co-authored 90 publications receiving 5033 citations. Previous affiliations of Hagai Attias include University of California, San Francisco & University College London.
Papers
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Proceedings Article
A Variational Baysian Framework for Graphical Models
TL;DR: This paper presents a novel practical framework for Bayesian model averaging and model selection in probabilistic graphical models that approximates full posterior distributions over model parameters and structures, as well as latent variables, in an analytical manner.
Proceedings Article
Inferring parameters and structure of latent variable models by variational bayes
TL;DR: The Variational Bayes framework as discussed by the authors approximates full posterior distributions over model parameters and structures, as well as latent variables, in an analytical manner without resorting to sampling methods.
Journal ArticleDOI
Independent factor analysis
TL;DR: An expectation-maximization (EM) algorithm is presented, which performs unsupervised learning of an associated probabilistic model of the mixing situation and is shown to be superior to ICA since it can learn arbitrary source densities from the data.
Journal ArticleDOI
Blind Source Separation Exploiting Higher-Order Frequency Dependencies
TL;DR: A new algorithm is proposed that exploits higher order frequency dependencies of source signals in order to separate them when they are mixed and outperforms the others in most cases.
Proceedings ArticleDOI
Topic regression multi-modal Latent Dirichlet Allocation for image annotation
TL;DR: The proposed association model shows improved performance over correspondence LDA as measured by caption perplexity, and a novel latent variable regression approach to capture correlations between image or video features and annotation texts.