References in zbMATH (referenced in 39 articles )

Showing results 1 to 20 of 39.
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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. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  6. Eric Horton, Chris Parnin: DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets (2019) arXiv
  7. 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
  8. Kaiyang Zhou, Tao Xiang: Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch (2019) arXiv
  9. Kossaifi, Jean; Panagakis, Yannis; Anandkumar, Anima; Pantic, Maja: TensorLy: tensor learning in Python (2019)
  10. Krishna Murthy Jatavallabhula, Edward Smith, Jean-Francois Lafleche, Clement Fuji Tsang, Artem Rozantsev, Wenzheng Chen, Tommy Xiang, Rev Lebaredian, Sanja Fidler: Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research (2019) arXiv
  11. Marco Melis, Ambra Demontis, Maura Pintor, Angelo Sotgiu, Battista Biggio: secml: A Python Library for Secure and Explainable Machine Learning (2019) arXiv
  12. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  13. Roussillon, Pierre; Glaunès, Joan Alexis: Representation of surfaces with normal cycles and application to surface registration (2019)
  14. Sergey Kolesnikov, Oleksii Hrinchuk: Catalyst.RL: A Distributed Framework for Reproducible RL Research (2019) arXiv
  15. 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
  16. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  17. 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
  18. Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio: Torchmeta: A Meta-Learning library for PyTorch (2019) arXiv
  19. van den Berg, E.: The Ocean Tensor Package (2019) not zbMATH
  20. Wang, Qiansheng; Yu, Nan; Zhang, Meishan; Han, Zijia; Fu, Guohong: N3LDG: a lightweight neural network library for natural language processing (2019)

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