PyTorch

PyTorch python package: Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a deep learning framework that puts Python first.

Keywords for this software

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References in zbMATH (referenced in 170 articles )

Showing results 1 to 20 of 170.
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  1. Binbin Zhang, Di Wu, Chao Yang, Xiaoyu Chen, Zhendong Peng, Xiangming Wang, Zhuoyuan Yao, Xiong Wang, Fan Yu, Lei Xie, Xin Lei: WeNet: Production First and Production Ready End-to-End Speech Recognition Toolkit (2021) arXiv
  2. Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, Rodrigo Nogueira: Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations (2021) arXiv
  3. Jun Wang, Yinglu Liu, Yibo Hu, Hailin Shi, Tao Mei: FaceX-Zoo: A PyTorch Toolbox for Face Recognition (2021) arXiv
  4. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  5. Lu, Lu; Meng, Xuhui; Mao, Zhiping; Karniadakis, George Em: DeepXDE: a deep learning library for solving differential equations (2021)
  6. Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Zhao Xu, Haiyang Yu, Jingtun Zhang, Yi Liu, Keqiang Yan, Bora Oztekin, Haoran Liu, Xuan Zhang, Cong Fu, Shuiwang Ji: DIG: A Turnkey Library for Diving into Graph Deep Learning Research (2021) arXiv
  7. Tang, H. S.; Li, L.; Grossberg, M.; Liu, Y. J.; Jia, Y. M.; Li, S. S.; Dong, W. B.: An exploratory study on machine learning to couple numerical solutions of partial differential equations (2021)
  8. Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido van de Ven, Martin Mundt, Qi She, Keiland Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German I. Parisi, Fabio Cuzzolin, Andreas Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni: Avalanche: an End-to-End Library for Continual Learning (2021) arXiv
  9. Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang: CogDL: An Extensive Toolkit for Deep Learning on Graphs (2021) arXiv
  10. Alain Jungo, Olivier Scheidegger, Mauricio Reyes, Fabian Balsiger: pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis (2020) arXiv
  11. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  12. 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)
  13. Alvaro Tejero-Canteroe; Jan Boeltse; Michael Deistlere; Jan-Matthis Lueckmanne; Conor Durkane; Pedro J. Gonçalves; David S. Greenberg; Jakob H. Macke: sbi: A toolkit for simulation-based inference (2020) not zbMATH
  14. Andreux, Mathieu; Angles, Tomás; Exarchakis, Georgios; Leonarduzzi, Roberto; Rochette, Gaspar; Thiry, Louis; Zarka, John; Mallat, Stéphane; Andén, Joakim; Belilovsky, Eugene; Bruna, Joan; Lostanlen, Vincent; Chaudhary, Muawiz; Hirn, Matthew J.; Oyallon, Edouard; Zhang, Sixin; Cella, Carmine; Eickenberg, Michael: Kymatio: scattering transforms in Python (2020)
  15. Ankit, Aayush; El Hajj, Izzat; Chalamalasetti, Sai Rahul; Agarwal, Sapan; Marinella, Matthew; Foltin, Martin; Strachan, John Paul; Milojicic, Dejan; Hwu, Wen-Mei; Roy, Kaushik: PANTHER: a programmable architecture for neural network training harnessing energy-efficient ReRAM (2020)
  16. Bacciu, Davide; Errica, Federico; Micheli, Alessio: Probabilistic learning on graphs via contextual architectures (2020)
  17. Baguer, Daniel Otero; Leuschner, Johannes; Schmidt, Maximilian: Computed tomography reconstruction using deep image prior and learned reconstruction methods (2020)
  18. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  19. Benyi Hu, Ren-Jie Song, Xiu-Shen Wei, Yazhou Yao, Xian-Sheng Hua, Yuehu Liu: PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks (2020) arXiv
  20. Berahas, Albert S.; Takáč, Martin: A robust multi-batch L-BFGS method for machine learning (2020)

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