MXNet

MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers


References in zbMATH (referenced in 25 articles )

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  1. Alexandrov, Alexander; Benidis, Konstantinos; Bohlke-Schneider, Michael; Flunkert, Valentin; Gasthaus, Jan; Januschowski, Tim; Maddix, Danielle C.; Rangapuram, Syama; Salinas, David; Schulz, Jasper; Stella, Lorenzo; Türkmen, Ali Caner; Wang, Yuyang: GluonTS: probabilistic and neural time series modeling in Python (2020)
  2. Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr: FedML: A Research Library and Benchmark for Federated Machine Learning (2020) arXiv
  3. 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)
  4. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  5. Kossaifi, Jean; Lipton, Zachary C.; Kolbeinsson, Arinbjorn; Khanna, Aran; Furlanello, Tommaso; Anandkumar, Anima: Tensor regression networks (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. Janzamin, Majid; Ge, Rong; Kossaifi, Jean; Anandkumar, Anima: Spectral learning on matrices and tensors (2019)
  8. Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin: MMDetection: Open MMLab Detection Toolbox and Benchmark (2019) arXiv
  9. van den Berg, E.: The Ocean Tensor Package (2019) not zbMATH
  10. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  11. Xiaomeng Dong, Junpyo Hong, Hsi-Ming Chang, Michael Potter, Aritra Chowdhury, Purujit Bahl, Vivek Soni, Yun-Chan Tsai, Rajesh Tamada, Gaurav Kumar, Caroline Favart, V. Ratna Saripalli, Gopal Avinash: FastEstimator: A Deep Learning Library for Fast Prototyping and Productization (2019) arXiv
  12. Zhao-Yun Chen, Cheng Xue, Si-Ming Chen, Guo-Ping Guo: VQNet: Library for a Quantum-Classical Hybrid Neural Network (2019) arXiv
  13. Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
  14. Andrew Beers; James Brown; Ken Chang; Katharina Hoebel; Elizabeth Gerstner; Bruce Rosen; Jayashree Kalpathy-Cramer: DeepNeuro: an open-source deep learning toolbox for neuroimaging (2018) arXiv
  15. 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
  16. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
  17. 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
  18. Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson: GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration (2018) arXiv
  19. Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki: RLgraph: Modular Computation Graphs for Deep Reinforcement Learning (2018) arXiv
  20. Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post: Sockeye: A Toolkit for Neural Machine Translation (2017) arXiv

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