Machine learningDeep learning / NLP / CV
LDA Topic Model
Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.
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Domain-adaptive NMF Topic ModelExplainable NMF Topic ModelExplainable Topic ModelingFine-Tuned LDA Topic ModelFine-Tuned Topic ModelingFine-Tuned Word2VecMultilingual Doc2VecMultilingual topic modelingMultimodal LDA topic modelMultimodal Topic ModelingNMF Topic ModelSelf-supervised LDA Topic ModelSelf-supervised topic modelingSemi-supervised LDA Topic ModelSemi-supervised NMF Topic ModelSemi-supervised Sentiment AnalysisSemi-supervised Word2VecTopic ModelingTransfer Learning with LDA Topic ModelTransfer Learning with NMF Topic ModelTransfer Learning with Topic ModelingTransfer Learning with Word2VecWeakly supervised LDA topic modelWeakly Supervised Topic Modeling