Linear Time Samplers for Supervised Topic Models using Compositional Proposals

ACM Knowledge Discovery and Data Mining, pp. 1523-1532, 2015.

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Abstract:

Topic models are effective probabilistic tools for processing large collections of unstructured data. With the exponential growth of modern industrial data, and consequentially also with our ambition to explore much bigger models, there is a real pressing need to significantly scale up topic modeling algorithms, which has been taken up in...More

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