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 20 articles )

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

  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. Kossaifi, Jean; Panagakis, Yannis; Anandkumar, Anima; Pantic, Maja: TensorLy: tensor learning in Python (2019)
  6. 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
  7. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  8. van den Berg, E.: The Ocean Tensor Package (2019) not zbMATH
  9. Zhao-Yun Chen, Cheng Xue, Si-Ming Chen, Guo-Ping Guo: VQNet: Library for a Quantum-Classical Hybrid Neural Network (2019) arXiv
  10. Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
  11. Hongteng Xu: PoPPy: A Point Process Toolbox Based on PyTorch (2018) arXiv
  12. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  13. Innocenti, Luca; Banchi, Leonardo; Bose, Sougato; Ferraro, Alessandro; Paternostro, Mauro: Approximate supervised learning of quantum gates via ancillary qubits (2018)
  14. Jie Yang; Yue Zhang: NCRF++: An Open-source Neural Sequence Labeling Toolkit (2018) arXiv
  15. K.T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller: SchNetPack: A Deep Learning Toolbox For Atomistic Systems (2018) arXiv
  16. Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Matthew J. Hirn, Edouard Oyallon, Sixhin Zhang, Carmine Cella, Michael Eickenberg: Kymatio: Scattering Transforms in Python (2018) arXiv
  17. Oleksii Kuchaiev; Boris Ginsburg; Igor Gitman; Vitaly Lavrukhin; Carl Case; Paulius Micikevicius: OpenSeq2Seq: extensible toolkit for distributed and mixed precision training of sequence-to-sequence models (2018) arXiv
  18. Jonas Rauber, Wieland Brendel, Matthias Bethge: Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models (2017) arXiv
  19. Richard Wei, Vikram Adve, Lane Schwartz: DLVM: A modern compiler infrastructure for deep learning systems (2017) arXiv
  20. Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic: TensorLy: Tensor Learning in Python (2016) arXiv