Determinantal point processes for machine learning

2012-07-25Code Available0· sign in to hype

Alex Kulesza, Ben Taskar

Code Available — Be the first to reproduce this paper.

Code

Abstract

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for sampling, marginalization, conditioning, and other inference tasks. We provide a gentle introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and show how DPPs can be applied to real-world applications like finding diverse sets of high-quality search results, building informative summaries by selecting diverse sentences from documents, modeling non-overlapping human poses in images or video, and automatically building timelines of important news stories.

Tasks

BIG-bench Machine Learning Point Processes

Determinantal point processes for machine learning

Code

Abstract

Tasks

Reproductions