Open AccessProceedings Article
Kernel Topic Models
Philipp Hennig,David Stern,Ralf Herbrich,Thore Graepel +3 more
- 21 Mar 2012
- pp 511-519
TL;DR: An approximate algorithm cast around a Laplace approximation in a transformed basis is presented, which 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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
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.
652
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.
Deep Graph Generators: A Survey
TL;DR: A comprehensive survey on deep learning-based graph generation approaches is presented in this paper, where the authors classify them into five broad categories: autoregressive, autoencoder-based, reinforcement learning based, adversarial, and flow-based.
•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.
60
Machine Learning and Data Mining Approaches to Climate Science
Valliappa Lakshmanan,Eric Gilleland,Amy McGovern,Martin Tingley +3 more
- 01 Jan 2015
TL;DR: This paper explores an alternative approach to reconstruct the space-time variations of a geophysical system from observations using multiple runs of the known dynamical model using machine learning and data mining methods.
58
References
Latent dirichlet allocation
TL;DR: This work proposes 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 Hofmann's aspect model.
Gaussian Processes For Machine Learning
Tanja Hueber
- 01 Jan 2016
TL;DR: The gaussian processes for machine learning is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can get it instantly.
10K
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.
Related Papers (5)
[...]
David M. Blei,John Lafferty +1 more
- 25 Jun 2006
John Lafferty,David M. Blei +1 more
- 05 Dec 2005