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.


References in zbMATH (referenced in 263 articles )

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  1. Ali, Mehdi; Berrendorf, Max; Hoyt, Charles Tapley; Vermue, Laurent; Sharifzadeh, Sahand; Tresp, Volker; Lehmann, Jens: PyKEEN 1.0: a Python library for training and evaluating knowledge graph embeddings (2021)
  2. Anderson, Lara B.; Gerdes, Mathis; Gray, James; Krippendorf, Sven; Raghuram, Nikhil; Ruehle, Fabian: Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning (2021)
  3. Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis: ADAPT : Awesome Domain Adaptation Python Toolbox (2021) arXiv
  4. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  5. Bakhtin, Anton; Deng, Yuntian; Gross, Sam; Ott, Myle; Ranzato, Marc’aurelio; Szlam, Arthur: Residual energy-based models for text (2021)
  6. Baskerville, Nicholas P.; Keating, Jonathan P.; Mezzadri, Francesco; Najnudel, Joseph: The loss surfaces of neural networks with general activation functions (2021)
  7. 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
  8. Bolte, Jérôme; Pauwels, Edouard: Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning (2021)
  9. Cao, Shuai; Song, Biao: Visual attentional-driven deep learning method for flower recognition (2021)
  10. Celledoni, Elena; Ehrhardt, Matthias J.; Etmann, Christian; Owren, Brynjulf; Schönlieb, Carola-Bibiane; Sherry, Ferdia: Equivariant neural networks for inverse problems (2021)
  11. Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu: AutoGL: A Library for Automated Graph Learning (2021) arXiv
  12. Chengyuan Xu, Curtis McCully, Boning Dong, D. Andrew Howell, Pradeep Sen: Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit (2021) arXiv
  13. Chen, Li-Wei; Cakal, Berkay A.; Hu, Xiangyu; Thuerey, Nils: Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates (2021)
  14. Chen, Siyu; Glioti, Alfredo; Panico, Giuliano; Wulzer, Andrea: Parametrized classifiers for optimal EFT sensitivity (2021)
  15. Cho, Sung Woong; Hwang, Hyung Ju; Son, Hwijae: Traveling wave solutions of partial differential equations via neural networks (2021)
  16. Christopher P. Bridge, Chris Gorman, Steven Pieper, Sean W. Doyle, Jochen K. Lennerz, Jayashree Kalpathy-Cramer, David A. Clunie, Andriy Y. Fedorov, Markus D. Herrmann: Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology (2021) arXiv
  17. Christopher Schröder, Lydia Müller, Andreas Niekler, Martin Potthast: Small-text: Active Learning for Text Classification in Python (2021) arXiv
  18. Fan, Jianqing; Ma, Cong; Zhong, Yiqiao: A selective overview of deep learning (2021)
  19. Fanthomme, Arnaud; Monasson, Rémi: Low-dimensional manifolds support multiplexed integrations in recurrent neural networks (2021)
  20. Forgione, Marco; Piga, Dario: Continuous-time system identification with neural networks: model structures and fitting criteria (2021)

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