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Displaying 1-5 out of 5 total
Deep Learning with Hierarchical Convolutional Factor Analysis
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Bo Chen,Gungor Polatkan,Guillermo Sapiro,David Blei,David Dunson,Lawrence Carin
Issue Date:August 2013
pp. 1887-1901
Unsupervised multilayered (“deep”) models are considered for imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent...
A Bayesian Nonparametric Approach to Image Super-resolution
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Gungor Polatkan,David Blei,Ingrid Daubechies,Lawrence Carin,Mingyuan Zhou
Issue Date:May 2014
pp. 1
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called...
Distance Dependent Infinite Latent Feature Models
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Samuel Gershman,Peter Frazier,David Blei
Issue Date:May 2014
pp. 1
Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buet process (IBP) as a prior over latent features, allow the number of features to be determin...
Probabilistic topic models
Found in: Proceedings of the 17th ACM SIGKDD International Conference Tutorials (KDD '11 Tutorials)
By David Blei
Issue Date:August 2011
pp. 1-1
Probabilistic topic modeling provides a suite of tools for the unsupervised analysis of large collections of documents. Topic modeling algorithms can uncover the underlying themes of a collection and decompose its documents according to those themes. This ...
A latent mixed membership model for relational data
Found in: Proceedings of the 3rd international workshop on Link discovery (LinkKDD '05)
By David Blei, Edoardo Airoldi, Eric Xing, Stephen Fienberg
Issue Date:August 2005
pp. 82-89
Modeling relational data is an important problem for modern data analysis and machine learning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn l...