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 31 articles )

Showing results 1 to 20 of 31.
Sorted by year (citations)

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  1. Bart Theeten, Frederik Vandeputte, Tom Van Cutsem: Import2vec - Learning Embeddings for Software Libraries (2019) arXiv
  2. Bingham, Eli; Chen, Jonathan P.; Jankowiak, Martin; Obermeyer, Fritz; Pradhan, Neeraj; Karaletsos, Theofanis; Singh, Rohit; Szerlip, Paul; Horsfall, Paul; Goodman, Noah D.: Pyro: deep universal probabilistic programming (2019)
  3. Constantin Steppa, Tim L. Holch: HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch (2019) not zbMATH
  4. Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viegas, Martin Wattenberg: TensorFlow.js: Machine Learning for the Web and Beyond (2019) arXiv
  5. Eric Horton, Chris Parnin: DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets (2019) arXiv
  6. 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
  7. Kaiyang Zhou, Tao Xiang: Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch (2019) arXiv
  8. Kossaifi, Jean; Panagakis, Yannis; Anandkumar, Anima; Pantic, Maja: TensorLy: tensor learning in Python (2019)
  9. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  10. Roussillon, Pierre; Glaunès, Joan Alexis: Representation of surfaces with normal cycles and application to surface registration (2019)
  11. Sil C. van de Leemput; Jonas Teuwen; Bram van Ginneken; Rashindra Manniesing: MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks (2019) not zbMATH
  12. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  13. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Jamie Brew: HuggingFace’s Transformers: State-of-the-art Natural Language Processing (2019) arXiv
  14. Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio: Torchmeta: A Meta-Learning library for PyTorch (2019) arXiv
  15. van den Berg, E.: The Ocean Tensor Package (2019) not zbMATH
  16. Wang, Qiansheng; Yu, Nan; Zhang, Meishan; Han, Zijia; Fu, Guohong: N3LDG: a lightweight neural network library for natural language processing (2019)
  17. Zhao-Yun Chen, Cheng Xue, Si-Ming Chen, Guo-Ping Guo: VQNet: Library for a Quantum-Classical Hybrid Neural Network (2019) arXiv
  18. Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
  19. 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
  20. 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

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