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We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collections, including those arriving in a stream. We study the performance of online LDA in several ways, including by fitting a 100-topic topic model to 3.3M articles from Wikipedia in a single pass. We demonstrate that online LDA finds topic models as good or better than those found with batch VB, and in a fraction of the time.
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Video Length: 0
Date Found: March 25, 2011
Date Produced: March 25, 2011
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VideoLectures |
July 10, 2011
The explosion in growth of the Web of Linked Data has provided, for the first time, a plethora of information in disparate locations, yet bound together by machine-readable, semantically typed relations. Utilisation of the Web of Data has been, until now, restricted to members of the community, ...
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VideoLectures |
July 10, 2011
Problems cannot be solved with the mentality that has caused them’. Hence, the 2008- crisis cannot be solved with ethics of one-sided and short-term mentality of the industrial and neoliberal economics, which has caused the ‘Bubble Economy’ of several recent decades. Neither the market nor the ...
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VideoLectures |
July 10, 2011
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VideoLectures |
July 10, 2011
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VideoLectures |
July 10, 2011
Social media presents unique challenges for topic classification, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a conversation. In this paper we investigate the usefulness of these external hyperlinks ...
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