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 141 to 160 of 170.
Sorted by year (citations)

previous 1 2 3 ... 6 7 8 9 next

  1. van den Berg, E.: The Ocean Tensor Package (2019) not zbMATH
  2. Wang, Qiansheng; Yu, Nan; Zhang, Meishan; Han, Zijia; Fu, Guohong: N3LDG: a lightweight neural network library for natural language processing (2019)
  3. Xiaomeng Dong, Junpyo Hong, Hsi-Ming Chang, Michael Potter, Aritra Chowdhury, Purujit Bahl, Vivek Soni, Yun-Chan Tsai, Rajesh Tamada, Gaurav Kumar, Caroline Favart, V. Ratna Saripalli, Gopal Avinash: FastEstimator: A Deep Learning Library for Fast Prototyping and Productization (2019) arXiv
  4. Yin, Penghang; Zhang, Shuai; Lyu, Jiancheng; Osher, Stanley; Qi, Yingyong; Xin, Jack: Blended coarse gradient descent for full quantization of deep neural networks (2019)
  5. Zhang, Yimeng; Lee, Tai Sing; Li, Ming; Liu, Fang; Tang, Shiming: Convolutional neural network models of V1 responses to complex patterns (2019)
  6. Zhaoheng Ni, Michael I Mandel: Onssen: an open-source speech separation and enhancement library (2019) arXiv
  7. Zhao-Yun Chen, Cheng Xue, Si-Ming Chen, Guo-Ping Guo: VQNet: Library for a Quantum-Classical Hybrid Neural Network (2019) arXiv
  8. Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (2019) arXiv
  9. Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
  10. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  11. Dan Moldovan, James M Decker, Fei Wang, Andrew A Johnson, Brian K Lee, Zachary Nado, D Sculley, Tiark Rompf, Alexander B Wiltschko: AutoGraph: Imperative-style Coding with Graph-based Performance (2018) arXiv
  12. Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma: BindsNET: A machine learning-oriented spiking neural networks library in Python (2018) arXiv
  13. Hongteng Xu: PoPPy: A Point Process Toolbox Based on PyTorch (2018) arXiv
  14. Ignatiev, Alexey; Morgado, Antonio; Marques-Silva, Joao: PySAT: A Python toolkit for prototyping with SAT oracles (2018)
  15. Innocenti, Luca; Banchi, Leonardo; Bose, Sougato; Ferraro, Alessandro; Paternostro, Mauro: Approximate supervised learning of quantum gates via ancillary qubits (2018)
  16. Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson: GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration (2018) arXiv
  17. Jie Yang; Yue Zhang: NCRF++: An Open-source Neural Sequence Labeling Toolkit (2018) arXiv
  18. K.T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller: SchNetPack: A Deep Learning Toolbox For Atomistic Systems (2018) arXiv
  19. 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
  20. Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki: RLgraph: Modular Computation Graphs for Deep Reinforcement Learning (2018) arXiv

previous 1 2 3 ... 6 7 8 9 next