Name
Abstract | ||
|---|---|---|
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware. |
Year | Venue | Field |
|---|---|---|
2017 | Journal of Machine Learning Research | Front and back ends,CUDA,Computer science,Inference,Automatic differentiation,Exploit,Computational science,Gaussian process,Artificial intelligence,Deep learning,Python (programming language) |
DocType | Volume | Issue |
Journal | 18 | Issue-in-Progress |
ISSN | Citations | PageRank |
1532-4435 | 15 | 0.66 |
References | Authors | |
8 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander G. de G. Matthews | 1 | 49 | 2.72 |
| van der Wilk, Mark | 2 | 65 | 9.35 |
| Tom Nickson | 3 | 15 | 0.66 |
| Keisuke Fujii | 4 | 15 | 0.66 |
| A. Boukouvalas | 5 | 22 | 3.33 |
| Pablo León-Villagrá | 6 | 15 | 0.66 |
| Zoubin Ghahramani | 7 | 10455 | 1264.39 |
| James Hensman | 8 | 265 | 20.05 |