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Thursday, May 20 | 2 PM EST
Advances in scalable machine learning have made it possible to efficiently learn highly structured probabilistic models on large data sets. In this talk, Dr. John Paisley will discuss some of his work in this direction, including a review of probabilistic topic models for text and their extensions to tree and graph representations, as well as scalable model learning with stochastic variational inference.
This has significant implications when it comes to automatic labeling of documents, correlations between topics, exploring underlying themes of large troves of documents, and much more.
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