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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
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Probabilistic models of text and images
David M. Blei,Michael I. Jordan +1 more
TL;DR: A suite of probabilistic models of information collections for which the above problems can be cast as statistical queries are described, and directed graphical models are used as a flexible, modular framework for describing appropriate modeling assumptions about the data.
Proceedings Article
Content-based musical similarity computation using the hierarchical dirichlet process
TL;DR: A method for discovering the latent structure in MFCC feature data using the Hierarchical Dirichlet Process (HDP) and compute timbral similarity between recorded songs, which is faster than previous approaches that compare single Gaussian distributions directly.
Posted ContentDOI
Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans
TL;DR: This work develops state space models that decompose neural time-series into segments with simple, linear dynamics and incorporates these models into a hierarchical framework that combines partial recordings from many worms to learn shared structure, while still allowing for individual variability.
Journal ArticleDOI
De novo gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization.
Hanna Mendes Levitin,Jinzhou Yuan,Yim Ling Cheng,Francisco J. R. Ruiz,Francisco J. R. Ruiz,Erin C. Bush,Jeffrey N. Bruce,Peter Canoll,Antonio Iavarone,Anna Lasorella,David M. Blei,Peter A. Sims +11 more
TL;DR: ScHPF revealed an expression signature that was spatially biased toward the glioma‐infiltrated margins and associated with inferior survival in glioblastoma, and scHFP does not require prior normalization and captures statistical properties of single‐cell data better than other methods in benchmark datasets.
Proceedings Article
Hierarchical Implicit Models and Likelihood-Free Variational Inference
TL;DR: The hierarchical implicit models (HIMs) as discussed by the authors combine the idea of implicit densities with hierarchical Bayesian modeling to define models via simulators of data with rich hidden structure.