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 )

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  1. 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
  2. Raissi, Maziar: Deep hidden physics models: deep learning of nonlinear partial differential equations (2018)
  3. Shikhar Bhardwaj, Ryan R. Curtin, Marcus Edel, Yannis Mentekidis, Conrad Sanderson: ensmallen: a flexible C++ library for efficient function optimization (2018) arXiv
  4. Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, Tsubasa Ochiai: ESPnet: End-to-End Speech Processing Toolkit (2018) arXiv
  5. Tripathy, Rohit K.; Bilionis, Ilias: Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (2018)
  6. Yin, Penghang; Zhang, Shuai; Lyu, Jiancheng; Osher, Stanley; Qi, Yingyong; Xin, Jack: BinaryRelax: a relaxation approach for training deep neural networks with quantized weights (2018)
  7. Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica: RLlib: Abstractions for Distributed Reinforcement Learning (2017) arXiv
  8. Jonas Rauber, Wieland Brendel, Matthias Bethge: Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models (2017) arXiv
  9. Richard Wei, Vikram Adve, Lane Schwartz: DLVM: A modern compiler infrastructure for deep learning systems (2017) arXiv
  10. Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic: TensorLy: Tensor Learning in Python (2016) arXiv

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