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 170 articles )

Showing results 121 to 140 of 170.
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  1. 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
  2. Kvamme, Håvard; Borgan, Ørnulf; Scheel, Ida: Time-to-event prediction with neural networks and Cox regression (2019)
  3. Liu, Yimin; Sun, Wenyue; Durlofsky, Louis J.: A deep-learning-based geological parameterization for history matching complex models (2019)
  4. Marco Melis, Ambra Demontis, Maura Pintor, Angelo Sotgiu, Battista Biggio: secml: A Python Library for Secure and Explainable Machine Learning (2019) arXiv
  5. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  6. Montobbio, Noemi; Citti, Giovanna; Sarti, Alessandro: From receptive profiles to a metric model of V1 (2019)
  7. Neta Zmora, Guy Jacob, Lev Zlotnik, Bar Elharar, Gal Novik: Neural Network Distiller: A Python Package For DNN Compression Research (2019) arXiv
  8. Rawson, Michael; Reger, Giles: A neurally-guided, parallel theorem prover (2019)
  9. Roussillon, Pierre; Glaunès, Joan Alexis: Representation of surfaces with normal cycles and application to surface registration (2019)
  10. Saremi, Saeed; Hyvärinen, Aapo: Neural empirical Bayes (2019)
  11. Sellier, Jean Michel; Caron, Gaétan Marceau; Leygonie, Jacob: Signed particles and neural networks, towards efficient simulations of quantum systems (2019)
  12. Sergey Kolesnikov, Oleksii Hrinchuk: Catalyst.RL: A Distributed Framework for Reproducible RL Research (2019) arXiv
  13. 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
  14. Stöter, F.-R., Uhlich, S., Liutkus, A., Mitsufuji, Y: Open-Unmix - A Reference Implementation for Music Source Separation (2019) not zbMATH
  15. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  16. Tan, Jing; Chen, Chong-Bin: Deep learning the holographic black hole with charge (2019)
  17. Terenin, Alexander; Dong, Shawfeng; Draper, David: GPU-accelerated Gibbs sampling: a case study of the horseshoe probit model (2019)
  18. 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
  19. Tim Besard, Valentin Churavy, Alan Edelman, Bjorn De Sutter: Rapid software prototyping for heterogeneous and distributed platforms (2019) not zbMATH
  20. Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio: Torchmeta: A Meta-Learning library for PyTorch (2019) arXiv

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