The Discrete Infinite Logistic Normal Distribution

Chong Wang; David Blei; John Paisley

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The Discrete Infinite Logistic Normal Distribution

John Paisley, Chong Wang, David Blei

Machine Learning · Submitted 24 Mar 2011

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We study the empirical performance of the DILN topic model on four corpora, comparing performance with the HDP and the correlated topic model (CTM).

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,918 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.13 · human participants 0.03 · controlled comparison 0.74 · randomized assignment 0.01 · states quantitative result 0.72 · compares with prior results 0.98 · mentions code or data availability 0.02.

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arXiv listing abstract v3. Results and the main claim are underlined; show every role.

We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN as a normalized collection of gamma-distributed random variables, and study its statistical properties. We consider applications to topic modeling and derive a variational inference algorithm for approximate posterior inference. We study the empirical performance of the DILN topic model on four corpora, comparing performance with the HDP and the correlated topic model (CTM). To deal with large-scale data sets, we also develop an online inference algorithm for DILN and compare with online HDP and online LDA on the Nature magazine, which contains approximately 350,000 articles.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models.	contribution 0.98	0.91	0.16	asserted 1.00
s1	DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level.	contribution 0.52	0.45	0.11	asserted 1.00
s2	We derive a representation of DILN as a normalized collection of gamma-distributed random variables, and study its statistical properties.	contribution 0.75	0.97	0.08	asserted 1.00
s3	We consider applications to topic modeling and derive a variational inference algorithm for approximate posterior inference.	contribution 0.58	0.95	0.06	asserted 0.97
s4	We study the empirical performance of the DILN topic model on four corpora, comparing performance with the HDP and the correlated topic model (CTM).	contribution 0.81	0.96	0.15	asserted 0.94
s5	To deal with large-scale data sets, we also develop an online inference algorithm for DILN and compare with online HDP and online LDA on the Nature magazine, which contains approximately 350,000 articles.	contribution 0.68	0.96	0.07	asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.50.

What was examined, and what was not

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arXiv listing abstract v3

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full text
figures and tables
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P abstract

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S sections
T body sentences
C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN as a normalized collection of gamma-distributed random variables, and study its statistical properties. We consider applications to topic modeling and derive a variational inference algorithm for approximate posterior inference. We study the empirical performance of the DILN topic model on four corpora, comparing performance with the HDP and the correlated topic model (CTM). To deal with large-scale data sets, we also develop an online inference algorithm for DILN and compare with online HDP and online LDA on the Nature magazine, which contains approximately 350,000 articles.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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