1,192 research outputs found
The Discrete Infinite Logistic Normal Distribution
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.Comment: This paper will appear in Bayesian Analysis. A shorter version of
this paper appeared at AISTATS 2011, Fort Lauderdale, FL, US
Hierarchical relational models for document networks
We develop the relational topic model (RTM), a hierarchical model of both
network structure and node attributes. We focus on document networks, where the
attributes of each document are its words, that is, discrete observations taken
from a fixed vocabulary. For each pair of documents, the RTM models their link
as a binary random variable that is conditioned on their contents. The model
can be used to summarize a network of documents, predict links between them,
and predict words within them. We derive efficient inference and estimation
algorithms based on variational methods that take advantage of sparsity and
scale with the number of links. We evaluate the predictive performance of the
RTM for large networks of scientific abstracts, web documents, and
geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
Much of scientific progress stems from previously published findings, but
searching through the vast sea of scientific publications is difficult. We
often rely on metrics of scholarly authority to find the prominent authors but
these authority indices do not differentiate authority based on research
topics. We present Latent Topical-Authority Indexing (LTAI) for jointly
modeling the topics, citations, and topical authority in a corpus of academic
papers. Compared to previous models, LTAI differs in two main aspects. First,
it explicitly models the generative process of the citations, rather than
treating the citations as given. Second, it models each author's influence on
citations of a paper based on the topics of the cited papers, as well as the
citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS,
and Citeseer. We compare the performance of LTAI against various baselines,
starting with the latent Dirichlet allocation, to the more advanced models
including author-link topic model and dynamic author citation topic model. The
results show that LTAI achieves improved accuracy over other similar models
when predicting words, citations and authors of publications.Comment: Accepted by Transactions of the Association for Computational
Linguistics (TACL); to appea
Deep Exponential Families
We describe \textit{deep exponential families} (DEFs), a class of latent
variable models that are inspired by the hidden structures used in deep neural
networks. DEFs capture a hierarchy of dependencies between latent variables,
and are easily generalized to many settings through exponential families. We
perform inference using recent "black box" variational inference techniques. We
then evaluate various DEFs on text and combine multiple DEFs into a model for
pairwise recommendation data. In an extensive study, we show that going beyond
one layer improves predictions for DEFs. We demonstrate that DEFs find
interesting exploratory structure in large data sets, and give better
predictive performance than state-of-the-art models
Efficient Correlated Topic Modeling with Topic Embedding
Correlated topic modeling has been limited to small model and problem sizes
due to their high computational cost and poor scaling. In this paper, we
propose a new model which learns compact topic embeddings and captures topic
correlations through the closeness between the topic vectors. Our method
enables efficient inference in the low-dimensional embedding space, reducing
previous cubic or quadratic time complexity to linear w.r.t the topic size. We
further speedup variational inference with a fast sampler to exploit sparsity
of topic occurrence. Extensive experiments show that our approach is capable of
handling model and data scales which are several orders of magnitude larger
than existing correlation results, without sacrificing modeling quality by
providing competitive or superior performance in document classification and
retrieval.Comment: KDD 2017 oral. The first two authors contributed equall
Nested Hierarchical Dirichlet Processes
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical
topic modeling. The nHDP is a generalization of the nested Chinese restaurant
process (nCRP) that allows each word to follow its own path to a topic node
according to a document-specific distribution on a shared tree. This alleviates
the rigid, single-path formulation of the nCRP, allowing a document to more
easily express thematic borrowings as a random effect. We derive a stochastic
variational inference algorithm for the model, in addition to a greedy subtree
selection method for each document, which allows for efficient inference using
massive collections of text documents. We demonstrate our algorithm on 1.8
million documents from The New York Times and 3.3 million documents from
Wikipedia.Comment: To appear in IEEE Transactions on Pattern Analysis and Machine
Intelligence, Special Issue on Bayesian Nonparametric
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