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Parallel Latent Dirichlet Allocation on GPUs
2018Latent Dirichlet Allocation (LDA) is a statistical technique for topic modeling. Since it is very computationally demanding, its parallelization has garnered considerable interest. In this paper, we systematically analyze the data access patterns for LDA and devise suitable algorithmic adaptations and parallelization strategies for GPUs. Experiments on
Gordon E. Moon +5 more
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Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems, 2009Tagging systems have become major infrastructures on the Web. They allow users to create tags that annotate and categorize content and share them with other users, very helpful in particular for searching multimedia content. However, as tagging is not constrained by a controlled vocabulary and annotation guidelines, tags tend to be noisy and sparse ...
Ralf Krestel +2 more
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Latent Dirichlet Allocation Based Image Retrieval
2017In recent years, Bag-of-Visual-Word (BoVW) model has been widely used in computer vision. However, BoVW ignores not only spatial information but also semantic information between visual words. In this study, a latent Dirichlet allocation (LDA) based model has been proposed to obtain the semantic relations of visual words.
Jing Hao, Hongxi Wei
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Topic Modeling Using Latent Dirichlet allocation
ACM Computing Surveys, 2022Uttam Chauhan, Apurva Shah
exaly
Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation
Applied Energy, 2022Xiao Chen, Chad Zanocco, Ram Rajagopal
exaly