Open Access
Kernel Topic Models
Philipp Hennig,David Stern,Ralf Herbrich,Thore Graepel +3 more
- 01 Jan 2011
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TL;DR: In this paper, a variation of the Latent Dirichlet Allocation model is proposed, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions.
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Abstract: Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents’ mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is ecient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.
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Citations
A model of text for experimentation in the social sciences
TL;DR: A hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates is posit, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework.
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•Proceedings Article
Autoencoding Variational Inference for Topic Models
Akash Srivastava,Charles Sutton +1 more
- 26 Apr 2017
TL;DR: This paper proposed a new topic model called ProdLDA, which replaces the mixture model in LDA with a product of experts and showed that the new model yields much more interpretable topics.
Neural Models for Documents with Metadata
Dallas Card,Chenhao Tan,Noah A. Smith +2 more
- 01 Jul 2018
TL;DR: The authors proposed a general neural framework based on topic models to enable flexible incorporation of metadata and allow for rapid exploration of alternative models, which achieves strong performance, with a manageable tradeoff between perplexity, coherence, and sparsity.
•Proceedings Article
A temporal model of text periodicities using Gaussian Processes
Daniel Preoţiuc-Pietro,Trevor Cohn +1 more
- 01 Oct 2013
TL;DR: Gaussian Processes, a state-ofthe-art bayesian non-parametric model, with a novel periodic kernel is used, which is used for regression in order to forecast the volume of a hashtag based on past data.
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Personalized Employee Training Course Recommendation with Career Development Awareness
Chao Wang,Hengshu Zhu,Chen Zhu,Xi Zhang,Enhong Chen,Hui Xiong +5 more
- 20 Apr 2020
TL;DR: This paper proposes an explainable personalized online course recommender system for enhancing employee training and development based on a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN).
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References
•Proceedings Article
Latent Dirichlet Allocation
David M. Blei,Andrew Y. Ng,Michael I. Jordan +2 more
- 03 Jan 2001
TL;DR: This paper proposed 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, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Finding scientific topics
TL;DR: A generative model for documents is described, introduced by Blei, Ng, and Jordan, and a Markov chain Monte Carlo algorithm is presented for inference in this model, which is used to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics.
Dynamic topic models
David M. Blei,John Lafferty +1 more
- 25 Jun 2006
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.
•Proceedings Article
Online Learning for Latent Dirichlet Allocation
Matthew D. Hoffman,Francis Bach,David M. Blei +2 more
- 06 Dec 2010
TL;DR: An online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA) based on online stochastic optimization with a natural gradient step is developed, which shows converges to a local optimum of the VB objective function.
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