Name
Abstract | ||
|---|---|---|
Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.
|
Year | DOI | Keywords |
|---|---|---|
2019 | 10.1145/3292500.3330756 | computer vision, deep learning frameworks, distributed training, gpu computing |
Field | DocType | ISSN |
Graphics,Software engineering,Computer science,Artificial intelligence,Dynamic models,Acceleration,General-purpose computing on graphics processing units,Deep learning,Artificial neural network,Python (programming language),Machine learning,Software framework | Conference | 978-1-4503-6201-6 |
ISBN | Citations | PageRank |
978-1-4503-6201-6 | 7 | 0.57 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seiya Tokui | 1 | 23 | 2.65 |
| Ryosuke Okuta | 2 | 7 | 0.91 |
| Takuya Akiba | 3 | 378 | 20.70 |
| Yusuke Niitani | 4 | 15 | 1.39 |
| Toru Ogawa | 5 | 16 | 2.23 |
| Shunta Saito | 6 | 7 | 1.25 |
| Shuji Suzuki | 7 | 8 | 1.26 |
| Kota Uenishi | 8 | 7 | 0.57 |
| Brian Vogel | 9 | 7 | 0.91 |
| Hiroyuki Yamazaki Vincent | 10 | 7 | 0.57 |