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 390 articles )

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  1. Antonio Serrano-Muñoz, Nestor Arana-Arexolaleiba, Dimitrios Chrysostomou, Simon Bøgh: skrl: Modular and Flexible Library for Reinforcement Learning (2022) arXiv
  2. Baijiong Lin, Yu Zhang: LibMTL: A Python Library for Multi-Task Learning (2022) arXiv
  3. Blier-Wong, Christopher; Cossette, Hélène; Lamontagne, Luc; Marceau, Etienne: Geographic ratemaking with spatial embeddings (2022)
  4. Cheng, Lin; Wagner, Gregory J.: A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (2022)
  5. Dash, Tirtharaj; Srinivasan, Ashwin; Baskar, A.: Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (2022)
  6. Duru, Cihat; Alemdar, Hande; Baran, Ozgur Ugras: A deep learning approach for the transonic flow field predictions around airfoils (2022)
  7. Eigel, Martin; Gruhlke, Robert; Marschall, Manuel: Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion (2022)
  8. Frisch, Gabriel; Leger, Jean-Benoist; Grandvalet, Yves: Learning from missing data with the binary latent block model (2022)
  9. Goswami, Somdatta; Yin, Minglang; Yu, Yue; Karniadakis, George Em: A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (2022)
  10. Guo, Xu; Wang, Jiansen; Yang, Senlin; Ren, Yuxiao: Optimal staggered-grid finite-difference method for wave modeling based on artificial neural networks (2022)
  11. Jagtap, N. V.; Mudunuru, M. K.; Nakshatrala, K. B.: A deep learning modeling framework to capture mixing patterns in reactive-transport systems (2022)
  12. Jain, Niharika; Olmo, Alberto; Sengupta, Sailik; Manikonda, Lydia; Kambhampati, Subbarao: Imperfect imaGANation: implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses (2022)
  13. Jones, Corinne; Roulet, Vincent; Harchaoui, Zaid: Discriminative clustering with representation learning with any ratio of labeled to unlabeled data (2022)
  14. Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, George H. Chen, Zhihao Jia, Philip S. Yu: PyGOD: A Python Library for Graph Outlier Detection (2022) arXiv
  15. Kim, D. H.; Zohdi, T. I.: Tool path optimization of selective laser sintering processes using deep learning (2022)
  16. Knoblauch, Andreas: On the antiderivatives of (x^p/(1 - x)) with an application to optimize loss functions for classification with neural networks (2022)
  17. Kroer, Christian; Peysakhovich, Alexander; Sodomka, Eric; Stier-Moses, Nicolas E.: Computing large market equilibria using abstractions (2022)
  18. Lakhmiri, Dounia; Le Digabel, Sébastien: Use of static surrogates in hyperparameter optimization (2022)
  19. Lee, Sangseung; Yang, Jiasheng; Forooghi, Pourya; Stroh, Alexander; Bagheri, Shervin: Predicting drag on rough surfaces by transfer learning of empirical correlations (2022)
  20. Lindeberg, Tony: Scale-covariant and scale-invariant Gaussian derivative networks (2022)

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