Hierarchical Dirichlet Processes

By Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei. 2006

4,040 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Neural Variational Inference for Text Processing (2015, methodology): “Applications of these models on document modelling achieve significant improvements on generating documents, compared to conventional probabilistic topic models (Hofmann, 1999; Blei et al., 2003; Teh et al., 2006) and also the RBMs (Hinton & Salakhutdinov, 2009; Srivastava et al.”
Discovering objects and their location in images (2005, methodology): “Discussion: In the experiments it was necessary to specify the number of topics K, however Bayesian [20] or minimum complexity methods [3] can be used to infer the number of topics implied by a corpus.”
Learning with Hierarchical-Deep Models (2013, methodology): “nonparametric extension based on the HDP model [38],”
Recent Advances in Open Set Recognition: A Survey (2018, background): “Dirichlet process (DP) [120], [121], [122], [123], [124] considered as a distribution over distributions is a stochastic process, which has been widely applied in clustering and density estimation problems as a nonparametric prior defined over the number of mixture components.”
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation (2009, methodology): “To address this limitation, we add a clustering step within each of the images; this is similar to the structure of the hierarchical Dirichlet process (HDP) [21].”
LDA-based document models for ad-hoc retrieval (2006, methodology): “Latent Dirichlet Allocation or LDA (Blei et al, 2003), has quickly become one of the most popular probabilistic text modeling techniques in machine learning and has inspired a series of research papers (e.g., Girolami and Kaban, 2005; Teh et al, 2004).”
Cognitive control over learning: creating, clustering, and generalizing task-set structure. (2013, neutral): “Because it is a process model, it also affords predictions about the dynamics of action selection within a trial and, hence, response times.”
On Smoothing and Inference for Topic Models (2009, methodology): “Teh et al. [2007] formulate the CVB algorithm and empirically compare it to VB, while Mukherjee and Blei [2009] theoretically analyze the differ ences between VB and CVB and give cases for when CVB should perform better than VB. Welling et al. [2008b] also compare the algorithms and introduce a hybr”
Tractable Bayesian Inference of Time-Series Dependence Structure (2009, methodology): “Similarly one may adopt nonparametric Bayesian models such as the hierarchical Dirichlet process hidden Markov model (HDP-HMM)(Teh et al., 2006) to allow for an unknown number of potential revisited states.”
Exploiting Topic based Twitter Sentiment for Stock Prediction (2013, methodology): “LDA can learn a predefined number of topics and has been widely applied in its extended forms in sentiment analysis and many other tasks (Mei et al., 2007; Branavan et al., 2008; Lin and He, 2009; Zhao et al., 2010; Wang et al., 2010; Brody and Elhadad, 2010; Jo and Oh, 2011; Moghaddam and Ester, 20”
Reducing the sampling complexity of topic models (2014, methodology): “Likewise, whenever z indicates a term in a hierarchy, it leads to structured and mixed-content annotations [19, 2, 4, 12].”
A model of shared grasp affordances from demonstration (2007, methodology): “If this number is too small, the covariances for the position distribution of the affordance will be large, so it may take a number of samples to find one that is close enough to the object to successfully grasp it. The expected number of affordances used is a function of the number of data points a”
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology (2018, methodology): “Another strategy is to run a non-parametric topic model, such as a Hierarchical Dirichlet Process (HDP) topic model (see Teh, Jordan, Beal, & Blei, 2006) in which K does not need to be defined in advance.”
Novel Word-sense Identification (2014, methodology): “The Lau et al. (2012) WSI model is based on a Hierarchical Dirichlet Process (HDP, Teh et al., 2006), which is a non-parametric variant of a topic model that, like the commonly-used Latent Dirichlet Allo-cation (LDA, Blei et al., 2003), learns topics (in the form of multinomial probability…”
A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications (2017, methodology): “The themes were discovered using a nonparametric Bayesian approach [160], which clusters the documents together based on similarities.”