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
References in zbMATH (referenced in 390 articles )
Showing results 1 to 20 of 390.
Sorted by year (- Antonio Serrano-Muñoz, Nestor Arana-Arexolaleiba, Dimitrios Chrysostomou, Simon Bøgh: skrl: Modular and Flexible Library for Reinforcement Learning (2022) arXiv
- Baijiong Lin, Yu Zhang: LibMTL: A Python Library for Multi-Task Learning (2022) arXiv
- Blier-Wong, Christopher; Cossette, Hélène; Lamontagne, Luc; Marceau, Etienne: Geographic ratemaking with spatial embeddings (2022)
- Cheng, Lin; Wagner, Gregory J.: A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (2022)
- Dash, Tirtharaj; Srinivasan, Ashwin; Baskar, A.: Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (2022)
- Duru, Cihat; Alemdar, Hande; Baran, Ozgur Ugras: A deep learning approach for the transonic flow field predictions around airfoils (2022)
- Eigel, Martin; Gruhlke, Robert; Marschall, Manuel: Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion (2022)
- Frisch, Gabriel; Leger, Jean-Benoist; Grandvalet, Yves: Learning from missing data with the binary latent block model (2022)
- Goswami, Somdatta; Yin, Minglang; Yu, Yue; Karniadakis, George Em: A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (2022)
- Guo, Xu; Wang, Jiansen; Yang, Senlin; Ren, Yuxiao: Optimal staggered-grid finite-difference method for wave modeling based on artificial neural networks (2022)
- Jagtap, N. V.; Mudunuru, M. K.; Nakshatrala, K. B.: A deep learning modeling framework to capture mixing patterns in reactive-transport systems (2022)
- 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)
- Jones, Corinne; Roulet, Vincent; Harchaoui, Zaid: Discriminative clustering with representation learning with any ratio of labeled to unlabeled data (2022)
- 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
- Kim, D. H.; Zohdi, T. I.: Tool path optimization of selective laser sintering processes using deep learning (2022)
- Knoblauch, Andreas: On the antiderivatives of (x^p/(1 - x)) with an application to optimize loss functions for classification with neural networks (2022)
- Kroer, Christian; Peysakhovich, Alexander; Sodomka, Eric; Stier-Moses, Nicolas E.: Computing large market equilibria using abstractions (2022)
- Lakhmiri, Dounia; Le Digabel, Sébastien: Use of static surrogates in hyperparameter optimization (2022)
- Lee, Sangseung; Yang, Jiasheng; Forooghi, Pourya; Stroh, Alexander; Bagheri, Shervin: Predicting drag on rough surfaces by transfer learning of empirical correlations (2022)
- Lindeberg, Tony: Scale-covariant and scale-invariant Gaussian derivative networks (2022)