Chainer: a next-generation open source framework for deep learning. Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter.

References in zbMATH (referenced in 15 articles )

Showing results 1 to 15 of 15.
Sorted by year (citations)

  1. Itoh, Takeshi D.; Ishihara, Koji; Morimoto, Jun: Implicit contact dynamics modeling with explicit inertia matrix representation for real-time, model-based control in physical environment (2022)
  2. Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yaxin Gu, George Karypis: DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science (2021) arXiv
  3. Chrysos, Grigorios G.; Kossaifi, Jean; Zafeiriou, Stefanos: RoCGAN: robust conditional GAN (2020)
  4. Guo, Jian; He, He; He, Tong; Lausen, Leonard; Li, Mu; Lin, Haibin; Shi, Xingjian; Wang, Chenguang; Xie, Junyuan; Zha, Sheng; Zhang, Aston; Zhang, Hang; Zhang, Zhi; Zhang, Zhongyue; Zheng, Shuai; Zhu, Yi: GluonCV and GluonNLP: deep learning in computer vision and natural language processing (2020)
  5. Ruehle, Fabian: Data science applications to string theory (2020)
  6. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  7. Halverson, James; Nelson, Brent; Ruehle, Fabian: Branes with brains: exploring string vacua with deep reinforcement learning (2019)
  8. Kawamoto, Tatsuro; Tsubaki, Masashi; Obuchi, Tomoyuki: Mean-field theory of graph neural networks in graph partitioning (2019)
  9. Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz: Neural Tangents: Fast and Easy Infinite Neural Networks in Python (2019) arXiv
  10. Yasuhiro Fujita, Toshiki Kataoka, Prabhat Nagarajan, Takahiro Ishikawa: ChainerRL: A Deep Reinforcement Learning Library (2019) arXiv
  11. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  12. Dan Moldovan, James M Decker, Fei Wang, Andrew A Johnson, Brian K Lee, Zachary Nado, D Sculley, Tiark Rompf, Alexander B Wiltschko: AutoGraph: Imperative-style Coding with Graph-based Performance (2018) arXiv
  13. Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma: BindsNET: A machine learning-oriented spiking neural networks library in Python (2018) arXiv
  14. Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, Tsubasa Ochiai: ESPnet: End-to-End Speech Processing Toolkit (2018) arXiv
  15. Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic: TensorLy: Tensor Learning in Python (2016) arXiv