Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks.
Sep 13, 2014 · Abstract:Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, ...
Topic modeling based on latent Dirichlet allocation. (LDA) has been a framework of choice to deal with mul- timodal data, such as in image annotation tasks.
In this paper, we propose a label-based multimodal topic (LB-MMT) model to jointly model text and image data tagged with multiple labels.
[PDF] Navigating the Local Modes of Big Data: The Case of Topic Models
scholar.harvard.edu › multimod
Despite the problems with multi-modality, mixture models are often more accurate descriptions for data generating processes than more traditional regression ...
Missing: Autoregressive | Show results with:Autoregressive
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Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks.
Topic modeling based on latent Dirichlet allocation. (LDA) has been a framework of choice to deal with mul- timodal data, such as in image annotation tasks.
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks.
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks.
A deep and autoregressive approach for topic modeling of multi- modal data. In IEEE transactions on pattern analysis and machine intelligence, 1056–1069.