#### Filter Results:

- Full text PDF available (205)

#### Publication Year

1982

2017

- This year (16)
- Last 5 years (117)
- Last 10 years (182)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Data Set Used

#### Key Phrases

#### Method

#### Organism

Learn More

- David M. Blei, Andrew Y. Ng, Michael I. Jordan
- Journal of Machine Learning Research
- 2003

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model , also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is… (More)

- David M. Blei, Lawrence Carin, David B. Dunson
- IEEE Signal Processing Magazine
- 2010

Probabilistic topic modeling provides a suite of tools for the unsupervised analysis of large collections of documents. Topic modeling algorithms can uncover the underlying themes of a collection and decompose its documents according to those themes. This analysis can be used for corpus exploration, document search, and a variety of prediction problems.
In… (More)

We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet… (More)

- David M. Blei, John D. Lafferty
- ICML
- 2006

A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed… (More)

Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Practitioners typically assume that the latent space is semantically meaningful. It is used to check models, summarize the corpus, and guide exploration of its contents.… (More)

- Matthew D. Hoffman, David M. Blei, Chong Wang, John William Paisley
- Journal of Machine Learning Research
- 2013

The distinction between local and global variables will be important for us to develop online inference. In Bayesian statistics, for example, think of β as parameters with a prior and z1:n as hidden variables which are individual to each observation. In a Bayesian mixture of Gaussians the global variables β are the mixture components and mixture… (More)

Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate… (More)

- David M. Blei, Jon D. McAuliffe
- NIPS
- 2007

We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we… (More)

- Matthew D. Hoffman, David M. Blei, Francis R. Bach
- NIPS
- 2010

We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collections, including those arriving in a stream. We study the… (More)

- Chong Wang, David M. Blei
- KDD
- 2011

Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our… (More)