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 )

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  1. Bertocchi, Carla; Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe; Prato, Marco: Deep unfolding of a proximal interior point method for image restoration (2020)
  2. Bloem-Reddy, Benjamin; Teh, Yee Whye: Probabilistic symmetries and invariant neural networks (2020)
  3. Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr: FedML: A Research Library and Benchmark for Federated Machine Learning (2020) arXiv
  4. Charley Gros, Andreanne Lemay, Olivier Vincent, Lucas Rouhier, Anthime Bucquet, Joseph Paul Cohen, Julien Cohen-Adad: ivadomed: A Medical Imaging Deep Learning Toolbox (2020) arXiv
  5. Chen, Yuansi; Taeb, Armeen; Bühlmann, Peter: A look at robustness and stability of (\ell_1)-versus (\ell_0)-regularization: discussion of papers by Bertsimas et al. and Hastie et al. (2020)
  6. Christoph Heindl, Lukas Brunner, Sebastian Zambal, Josef Scharinger: BlendTorch: A Real-Time, Adaptive Domain Randomization Library (2020) arXiv
  7. Ciosek, Kamil; Whiteson, Shimon: Expected policy gradients for reinforcement learning (2020)
  8. Cui, Ying; He, Ziyu; Pang, Jong-Shi: Multicomposite nonconvex optimization for training deep neural networks (2020)
  9. Daniel Deutsch, Dan Roth: SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics (2020) arXiv
  10. Davis, Damek; Drusvyatskiy, Dmitriy; Kakade, Sham; Lee, Jason D.: Stochastic subgradient method converges on tame functions (2020)
  11. Dehghani, Hamidreza; Zilian, Andreas: Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio (2020)
  12. Dempster, Angus; Petitjean, François; Webb, Geoffrey I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels (2020)
  13. de Vazelhes, William; Carey, Cj; Tang, Yuan; Vauquier, Nathalie; Bellet, Aurélien: metric-learn: metric learning algorithms in Python (2020)
  14. Dillon Niederhut: niacin: A Python package for text data enrichment (2020) not zbMATH
  15. Drori, Iddo: Deep variational inference (2020)
  16. Duan, Shiyu; Yu, Shujian; Chen, Yunmei; Principe, Jose C.: On kernel method-based connectionist models and supervised deep learning without backpropagation (2020)
  17. Engelhardt, Dalit: Dynamic control of stochastic evolution: a deep reinforcement learning approach to adaptively targeting emergent drug resistance (2020)
  18. Fangzhou Xie: Pruned Wasserstein Index Generation Model and wigpy Package (2020) arXiv
  19. 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
  20. 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

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