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 403 articles )

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  1. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  2. Angeli, Andrea; Desmet, Wim; Naets, Frank: Deep learning for model order reduction of multibody systems to minimal coordinates (2021)
  3. Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis: ADAPT : Awesome Domain Adaptation Python Toolbox (2021) arXiv
  4. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  5. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  6. Bobev, Nikolay; Fischbacher, Thomas; Gautason, Fridrik Freyr; Pilch, Krzysztof: New (\mathrmAdS_4) vacua in dyonic ISO(7) gauged supergravity (2021)
  7. Bolte, Jérôme; Pauwels, Edouard: Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning (2021)
  8. 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)
  9. Cao, Yongcan; Zhan, Huixin: Efficient multi-objective reinforcement learning via multiple-gradient descent with iteratively discovered weight-vector sets (2021)
  10. Carbonneau, Alexandre: Deep hedging of long-term financial derivatives (2021)
  11. Christopher P. Bridge, Chris Gorman, Steven Pieper, Sean W. Doyle, Jochen K. Lennerz, Jayashree Kalpathy-Cramer, David A. Clunie, Andriy Y. Fedorov, Markus D. Herrmann: Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology (2021) arXiv
  12. Dong, Bin; Wu, Kesheng; Byna, Suren: User-defined tensor data analysis (to appear) (2021)
  13. Eckstein, Stephan; Kupper, Michael: Computation of optimal transport and related hedging problems via penalization and neural networks (2021)
  14. Fan, Angela; Bhosale, Shruti; Schwenk, Holger; Ma, Zhiyi; El-Kishky, Ahmed; Goyal, Siddharth; Baines, Mandeep; Celebi, Onur; Wenzek, Guillaume; Chaudhary, Vishrav; Goyal, Naman; Birch, Tom; Liptchinsky, Vitaliy; Edunov, Sergey; Auli, Michael; Joulin, Armand: Beyond English-centric multilingual machine translation (2021)
  15. Fan, Jianqing; Ma, Cong; Zhong, Yiqiao: A selective overview of deep learning (2021)
  16. Feurer, Matthias; van Rijn, Jan N.; Kadra, Arlind; Gijsbers, Pieter; Mallik, Neeratyoy; Ravi, Sahithya; Müller, Andreas; Vanschoren, Joaquin; Hutter, Frank: OpenML-Python: an extensible Python API for OpenML (2021)
  17. Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado: Fast-DENSER: Fast Deep Evolutionary Network Structured Representation (2021) not zbMATH
  18. Forgione, Marco; Piga, Dario: Continuous-time system identification with neural networks: model structures and fitting criteria (2021)
  19. Freitas, Rodolfo S. M.; Barbosa, Carlos H. S.; Guerra, Gabriel M.; Coutinho, Alvaro L. G. A.; Rochinha, Fernando A.: An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty (2021)
  20. Fresca, Stefania; Dede’, Luca; Manzoni, Andrea: A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (2021)

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