TensorFlow

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 284 articles )

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  1. Alain Jungo, Olivier Scheidegger, Mauricio Reyes, Fabian Balsiger: pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis (2020) arXiv
  2. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  3. Alexandrov, Alexander; Benidis, Konstantinos; Bohlke-Schneider, Michael; Flunkert, Valentin; Gasthaus, Jan; Januschowski, Tim; Maddix, Danielle C.; Rangapuram, Syama; Salinas, David; Schulz, Jasper; Stella, Lorenzo; Türkmen, Ali Caner; Wang, Yuyang: GluonTS: probabilistic and neural time series modeling in Python (2020)
  4. Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi, Andrea Cavallaro: PrivEdge: From Local to Distributed Private Training and Prediction (2020) arXiv
  5. Anderson, Ross; Huchette, Joey; Ma, Will; Tjandraatmadja, Christian; Vielma, Juan Pablo: Strong mixed-integer programming formulations for trained neural networks (2020)
  6. Andreux, Mathieu; Angles, Tomás; Exarchakis, Georgios; Leonarduzzi, Roberto; Rochette, Gaspar; Thiry, Louis; Zarka, John; Mallat, Stéphane; Andén, Joakim; Belilovsky, Eugene; Bruna, Joan; Lostanlen, Vincent; Chaudhary, Muawiz; Hirn, Matthew J.; Oyallon, Edouard; Zhang, Sixin; Cella, Carmine; Eickenberg, Michael: Kymatio: scattering transforms in Python (2020)
  7. Anil, Robin; Capan, Gokhan; Drost-Fromm, Isabel; Dunning, Ted; Friedman, Ellen; Grant, Trevor; Quinn, Shannon; Ranjan, Paritosh; Schelter, Sebastian; Yılmazel, Özgür: Apache Mahout: machine learning on distributed dataflow systems (2020)
  8. Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann: From Things’ Modeling Language (ThingML) to Things’ Machine Learning (ThingML2) (2020) arXiv
  9. Arridge, S.; Hauptmann, A.: Networks for nonlinear diffusion problems in imaging (2020)
  10. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  11. Arya, Vijay; Bellamy, Rachel K. E.; Chen, Pin-Yu; Dhurandhar, Amit; Hind, Michael; Hoffman, Samuel C.; Houde, Stephanie; Liao, Q. Vera; Luss, Ronny; Mojsilović, Aleksandra; Mourad, Sami; Pedemonte, Pablo; Raghavendra, Ramya; Richards, John T.; Sattigeri, Prasanna; Shanmugam, Karthikeyan; Singh, Moninder; Varshney, Kush R.; Wei, Dennis; Zhang, Yunfeng: AI Explainability 360: an extensible toolkit for understanding data and machine learning models (2020)
  12. Baines, W.; Kuchment, P.; Ragusa, J.: Deep learning for 2D passive source detection in presence of complex cargo (2020)
  13. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  14. Bao, Gang; Ye, Xiaojing; Zang, Yaohua; Zhou, Haomin: Numerical solution of inverse problems by weak adversarial networks (2020)
  15. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  16. Biau, Gérard; Cadre, Benoît; Sangnier, Maxime; Tanielian, Ugo: Some theoretical properties of GANs (2020)
  17. Bloem-Reddy, Benjamin; Teh, Yee Whye: Probabilistic symmetries and invariant neural networks (2020)
  18. Bobev, Nikolay; Fischbacher, Thomas; Gautason, Fridrik Freyr; Pilch, Krzysztof: A cornucopia of (\mathrmAdS_5) vacua (2020)
  19. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  20. Boso, Francesca; Tartakovsky, Daniel M.: Data-informed method of distributions for hyperbolic conservation laws (2020)

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