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

References in zbMATH (referenced in 133 articles )

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  1. 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
  2. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  3. 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)
  4. 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
  5. 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)
  6. 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)
  7. Bacciu, Davide; Errica, Federico; Micheli, Alessio: Probabilistic learning on graphs via contextual architectures (2020)
  8. Baguer, Daniel Otero; Leuschner, Johannes; Schmidt, Maximilian: Computed tomography reconstruction using deep image prior and learned reconstruction methods (2020)
  9. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  10. 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
  11. Berahas, Albert S.; Takáč, Martin: A robust multi-batch L-BFGS method for machine learning (2020)
  12. Bertocchi, Carla; Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe; Prato, Marco: Deep unfolding of a proximal interior point method for image restoration (2020)
  13. Bloem-Reddy, Benjamin; Teh, Yee Whye: Probabilistic symmetries and invariant neural networks (2020)
  14. 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
  15. Charley Gros, Andreanne Lemay, Olivier Vincent, Lucas Rouhier, Anthime Bucquet, Joseph Paul Cohen, Julien Cohen-Adad: ivadomed: A Medical Imaging Deep Learning Toolbox (2020) arXiv
  16. Christoph Heindl, Lukas Brunner, Sebastian Zambal, Josef Scharinger: BlendTorch: A Real-Time, Adaptive Domain Randomization Library (2020) arXiv
  17. Ciosek, Kamil; Whiteson, Shimon: Expected policy gradients for reinforcement learning (2020)
  18. Cui, Ying; He, Ziyu; Pang, Jong-Shi: Multicomposite nonconvex optimization for training deep neural networks (2020)
  19. Daniel Deutsch, Dan Roth: SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics (2020) arXiv
  20. Davis, Damek; Drusvyatskiy, Dmitriy; Kakade, Sham; Lee, Jason D.: Stochastic subgradient method converges on tame functions (2020)

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