TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

References in zbMATH (referenced in 363 articles )

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  1. Angeli, Andrea; Desmet, Wim; Naets, Frank: Deep learning for model order reduction of multibody systems to minimal coordinates (2021)
  2. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  3. Canchumuni, Smith W. A.; Castro, Jose D. B.; Potratz, Júlia; Emerick, Alexandre A.; Pacheco, Marco Aurélio C.: Recent developments combining ensemble smoother and deep generative networks for facies history matching (2021)
  4. Cao, Yongcan; Zhan, Huixin: Efficient multi-objective reinforcement learning via multiple-gradient descent with iteratively discovered weight-vector sets (2021)
  5. Eckstein, Stephan; Kupper, Michael: Computation of optimal transport and related hedging problems via penalization and neural networks (2021)
  6. Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado: Fast-DENSER: Fast Deep Evolutionary Network Structured Representation (2021) not zbMATH
  7. Haghighat, Ehsan; Juanes, Ruben: SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (2021)
  8. Haghighat, Ehsan; Raissi, Maziar; Moure, Adrian; Gomez, Hector; Juanes, Ruben: A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (2021)
  9. Huré, Côme; Pham, Huyên; Bachouch, Achref; Langrené, Nicolas: Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis (2021)
  10. Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, Rodrigo Nogueira: Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations (2021) arXiv
  11. Jun Wang, Yinglu Liu, Yibo Hu, Hailin Shi, Tao Mei: FaceX-Zoo: A PyTorch Toolbox for Face Recognition (2021) arXiv
  12. Kalogeris, Ioannis; Papadopoulos, Vissarion: Diffusion maps-aided neural networks for the solution of parametrized PDEs (2021)
  13. Kharazmi, Ehsan; Zhang, Zhongqiang; Karniadakis, George E. M.: \textithp-VPINNs: variational physics-informed neural networks with domain decomposition (2021)
  14. Kim, Hyojin; Kim, Junhyuk; Won, Sungjin; Lee, Changhoon: Unsupervised deep learning for super-resolution reconstruction of turbulence (2021)
  15. Li, Rui; Reich, Brian J.; Bondell, Howard D.: Deep distribution regression (2021)
  16. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  17. Lu, Lu; Meng, Xuhui; Mao, Zhiping; Karniadakis, George Em: DeepXDE: a deep learning library for solving differential equations (2021)
  18. Lye, Kjetil O.; Mishra, Siddhartha; Ray, Deep; Chandrashekar, Praveen: Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks (2021)
  19. Paris, Romain; Beneddine, Samir; Dandois, Julien: Robust flow control and optimal sensor placement using deep reinforcement learning (2021)
  20. Patel, Ravi G.; Trask, Nathaniel A.; Wood, Mitchell A.; Cyr, Eric C.: A physics-informed operator regression framework for extracting data-driven continuum models (2021)

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