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 83 articles )

Showing results 1 to 20 of 83.
Sorted by year (citations)

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  1. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  2. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  3. Benyi Hu, Ren-Jie Song, Xiu-Shen Wei, Yazhou Yao, Xian-Sheng Hua, Yuehu Liu: PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks (2020) arXiv
  4. Berahas, Albert S.; Takáč, Martin: A robust multi-batch L-BFGS method for machine learning (2020)
  5. Bertocchi, Carla; Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe; Prato, Marco: Deep unfolding of a proximal interior point method for image restoration (2020)
  6. Cui, Ying; He, Ziyu; Pang, Jong-Shi: Multicomposite nonconvex optimization for training deep neural networks (2020)
  7. Davis, Damek; Drusvyatskiy, Dmitriy; Kakade, Sham; Lee, Jason D.: Stochastic subgradient method converges on tame functions (2020)
  8. Fangzhou Xie: Pruned Wasserstein Index Generation Model and wigpy Package (2020) arXiv
  9. Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi: DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments (2020) arXiv
  10. Feiyu Chen; David Sondak; Pavlos Protopapas; Marios Mattheakis; Shuheng Liu; Devansh Agarwal; Marco Di Giovanni: NeuroDiffEq: A Python package for solving differential equations with neural networks (2020) not zbMATH
  11. Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
  12. Frank Mancolo: Eisen: a python package for solid deep learning (2020) arXiv
  13. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)
  14. Kissas, Georgios; Yang, Yibo; Hwuang, Eileen; Witschey, Walter R.; Detre, John A.; Perdikaris, Paris: Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks (2020)
  15. Lukas Geiger; Plumerai Team: Larq: An Open-Source Library for Training Binarized Neural Networks (2020) not zbMATH
  16. Nguyen-Thanh, Vien Minh; Zhuang, Xiaoying; Rabczuk, Timon: A deep energy method for finite deformation hyperelasticity (2020)
  17. Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste: Bayesian active learning for production, a systematic study and a reusable library (2020) arXiv
  18. P.E. Hadjidoukas, A. Bartezzaghi, F. Scheidegger, R. Istrate, C.Bekas, A.C.I. Malossi: torcpy: Supporting task parallelism in Python (2020) not zbMATH
  19. Ruehle, Fabian: Data science applications to string theory (2020)
  20. Samaniego, E.; Anitescu, C.; Goswami, S.; Nguyen-Thanh, V. M.; Guo, H.; Hamdia, K.; Zhuang, X.; Rabczuk, Timon: An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications (2020)

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