Name
Abstract | ||
|---|---|---|
Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs is a challenge and therefore we present DPPy, a Python toolbox that gathers known exact and approximate sampling algorithms for both finite and continuous DPPs. The project is hosted on GitHubc) and equipped with an extensive documentation. |
Year | Venue | Keywords |
|---|---|---|
2019 | JOURNAL OF MACHINE LEARNING RESEARCH | determinantal point processes,sampling,MCMC,random matrices,Python |
DocType | Volume | Issue |
Journal | 20 | 180 |
ISSN | Citations | PageRank |
1532-4435 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gautier, Guillaume | 1 | 0 | 0.68 |
| Guillermo Polito | 2 | 2 | 7.84 |
| Rémi Bardenet | 3 | 350 | 16.90 |
| Michal Valko | 4 | 212 | 37.24 |