determinantal point process
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Composable Coresets for Determinant Maximization: Greedy is Almost Optimal
However, the most common algorithm used for this problem in practical applications is a natural greedy algorithm.
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DppNet: Approximating Determinantal Point Processes with Deep Networks
Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek
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Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
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Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
Laming Chen, Guoxin Zhang, Eric Zhou
In addition, our algorithm also adapts to scenarios where the repulsion is onlyrequired among nearby fewitems intheresult sequence.
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Learning Nonsymmetric Determinantal Point Processes
Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene
Neural Information Processing Systems http://nips.cc/
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2 Relatedwork Inthesection,wereviewsomeresultsthatarerelevanttothecontribution. 2.1 Ermakov-Zolotukhinquadrature ThequadratureruleproposedbyErmakovandZolotukhinin[14]dealswithintegralsthatwrite Z
Integrals appear in manyscientific fields as quantities of interest per se.
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