Learning determinantal point processes by corrective negative sampling
Determinantal Point Processes (DPPs) have attracted significant interest from the machine-learning community due to their ability to elegantly and tractably model the delicate balance between quality and diversity of sets. DPPs are commonly learned from data using maximum likelihood estimation (MLE)...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
MLResearch Press
2021
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| Online Access: | https://hdl.handle.net/1721.1/130415 |