Introduction to the Determinantal Point Process — Wray Buntine

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Representing objects with feature vectors lets us measure similarity using dot products. Using this notion, the determinantal point process (DPP) can be introduced as a distribution over objects maximising diversity. In this tutorial we will explore the DPP with the help of the visual analogies developed by Kulesza and Taskar in their tutorials and their 120 page Foundations and Trends article "determinantal point processes for machine learning."

Topics covered are interpretations and definitions, probability operations, such as marginalising and conditioning, and sampling.

The tutorial makes great use of the knowledge of matrices and
determinants.

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Brilliant explanation of DPPs. Thanks for sharing!

miguelguzman

The lecture on DPP is great. Could you please share the slides also.

gauravkumarnayak

The lecture could be better. The presenter is assuming people have good understanding of subset, determinant... also some contents were skipped which could be confusing for general audience. The pronunciation is not very clear.

kruan

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