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 199 articles )

Showing results 1 to 20 of 199.
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  1. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  2. 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
  3. 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
  4. Hernandez, Quercus; Badías, Alberto; González, David; Chinesta, Francisco; Cueto, Elías: Deep learning of thermodynamics-aware reduced-order models from data (2021)
  5. Hitoshi Manabe, Masato Hagiwara: EXPATS: A Toolkit for Explainable Automated Text Scoring (2021) arXiv
  6. 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
  7. Jun Wang, Yinglu Liu, Yibo Hu, Hailin Shi, Tao Mei: FaceX-Zoo: A PyTorch Toolbox for Face Recognition (2021) arXiv
  8. Kafka, Dominic; Wilke, Daniel N.: Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (2021)
  9. Kong Aik Lee, Ville Vestman, Tomi Kinnunen: ASVtorch toolkit: Speaker verification with deep neural networks (2021) not zbMATH
  10. Li, Zhihan; Fan, Yuwei; Ying, Lexing: Multilevel fine-tuning: closing generalization gaps in approximation of solution maps under a limited budget for training data (2021)
  11. Luca Demetrio, Battista Biggio: secml-malware: A Python Library for Adversarial Robustness Evaluation of Windows Malware Classifiers (2021) arXiv
  12. Lukas Heinrich; Matthew Feickert; Giordon Stark; Kyle Cranmer: pyhf: pure-Python implementation of HistFactory statistical models (2021) not zbMATH
  13. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  14. Lu, Lu; Meng, Xuhui; Mao, Zhiping; Karniadakis, George Em: DeepXDE: a deep learning library for solving differential equations (2021)
  15. Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan: A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays (2021) arXiv
  16. 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
  17. Minakowska, Martyna; Richter, Thomas; Sager, Sebastian: A finite element/neural network framework for modeling suspensions of non-spherical particles. Concepts and medical applications (2021)
  18. Ohnishi, Motoya; Notomista, Gennaro; Sugiyama, Masashi; Egerstedt, Magnus: Constraint learning for control tasks with limited duration barrier functions (2021)
  19. Patel, Ravi G.; Trask, Nathaniel A.; Wood, Mitchell A.; Cyr, Eric C.: A physics-informed operator regression framework for extracting data-driven continuum models (2021)
  20. Saha, Sourav; Gan, Zhengtao; Cheng, Lin; Gao, Jiaying; Kafka, Orion L.; Xie, Xiaoyu; Li, Hengyang; Tajdari, Mahsa; Kim, H. Alicia; Liu, Wing Kam: Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering (2021)

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