D
David M. Blei
Researcher at Columbia University
Publications - 399
Citations - 122384
David M. Blei is an academic researcher from Columbia University. The author has contributed to research in topics: Inference & Topic model. The author has an hindex of 98, co-authored 378 publications receiving 111547 citations. Previous affiliations of David M. Blei include Columbia University Medical Center & Hewlett-Packard.
Papers
More filters
Proceedings ArticleDOI
Dynamic topic models
David M. Blei,John Lafferty +1 more
TL;DR: A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections, and dynamic topic models provide a qualitative window into the contents of a large document collection.
Journal ArticleDOI
Stochastic variational inference
TL;DR: Stochastic variational inference lets us apply complex Bayesian models to massive data sets, and it is shown that the Bayesian nonparametric topic model outperforms its parametric counterpart.
Proceedings Article
Reading Tea Leaves: How Humans Interpret Topic Models
TL;DR: New quantitative methods for measuring semantic meaning in inferred topics are presented, showing that they capture aspects of the model that are undetected by previous measures of model quality based on held-out likelihood.
Journal ArticleDOI
Mixed Membership Stochastic Blockmodels
TL;DR: In this article, the authors introduce a class of variance allocation models for pairwise measurements, called mixed membership stochastic blockmodels, which combine global parameters that instantiate dense patches of connectivity (blockmodel) with local parameters (mixed membership), and develop a general variational inference algorithm for fast approximate posterior inference.
Journal ArticleDOI
Matching words and pictures
TL;DR: A new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text, is presented, and a number of models for the joint distribution of image regions and words are developed, including several which explicitly learn the correspondence between regions and Words.