Computer Vision Models Learning And Inference Bibtex Bibliography

Crossref Citations

This (lowercase (translateProductType product.productType)) has been cited by the following publications. This list is generated based on data provided by CrossRef.

Aoki, Terumasa and Nguyen, Van 2018. Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization. Advances in Multimedia, Vol. 2018, p. 1.

Panicheva, Polina Mirzagitova, Aliia and Ledovaya, Yanina 2018. Artificial Intelligence and Natural Language. Vol. 789, Issue. , p. 3.

Corral-Soto, Eduardo R. and Elder, James H. 2017. Slot Cars: 3D Modelling for Improved Visual Traffic Analytics. p. 889.

Bove, Anna Gradeci, Daniel Fujita, Yasuyuki Banerjee, Shiladitya Charras, Guillaume Lowe, Alan R. and Mogilner, Alex 2017. Local cellular neighborhood controls proliferation in cell competition. Molecular Biology of the Cell, Vol. 28, Issue. 23, p. 3215.

Takezawa, Yusuke Hasegawa, Makoto and Tabbone, Salvatore 2017. Robust Perspective Rectification of Camera-Captured Document Images. p. 27.

Rodin, Christopher Dahlin and Johansen, Tor Arne 2017. Accuracy of sea ice floe size observation from an aerial camera at slant angles. p. 216.

Savchenko, Andrey V. 2017. Clustering and maximum likelihood search for efficient statistical classification with medium-sized databases. Optimization Letters, Vol. 11, Issue. 2, p. 329.

Li, Na Mak, Man-Wai and Chien, Jen-Tzung 2017. DNN-Driven Mixture of PLDA for Robust Speaker Verification. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 25, Issue. 6, p. 1371.

Masutani, Yoshitaka Noriki, Sakon Kido, Shoji Arimura, Hidetaka Tomikawa, Morimasa Hontani, Hidekata and Sato, Yoshinobu 2017. Computational Anatomy Based on Whole Body Imaging. p. 1.

Rummelhard, Lukas Paigwar, Anshul Negre, Amaury and Laugier, Christian 2017. Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field. p. 1105.

Maldonado-Mendez, Carolina Solis, Ana Luisa Rios-Figueroa, Homero Vladimir and Marin-Hernandez, Antonio 2017. Human fallen pose detection by using feature selection and a generative model. p. 1.

Shapiro, Aaron 2017. Street-level: Google Street View’s abstraction by datafication. New Media & Society, p. 146144481668729.

Schwab, Michail Strobelt, Hendrik Tompkin, James Fredericks, Colin Huff, Connor Higgins, Dana Strezhnev, Anton Komisarchik, Mayya King, Gary and Pfister, Hanspeter 2017. An Education System with Hierarchical Concept Maps and Dynamic Non-linear Learning Plans. IEEE Transactions on Visualization and Computer Graphics, Vol. 23, Issue. 1, p. 571.

Sosa-Jimenez, Candy Obdulia Rios-Figueroa, Homero Vladimir Rechy-Ramirez, Ericka Janet Marin-Hernandez, Antonio and Gonzalez-Cosio, Ana Luisa Solis 2017. Real-time Mexican Sign Language recognition. p. 1.

Elassal, Nada and Elder, James H. 2017. Estimating Camera Tilt from Motion without Tracking. p. 72.

Breuvart, Flavien Dal Lago, Ugo and Herrou, Agathe 2017. Foundations of Software Science and Computation Structures. Vol. 10203, Issue. , p. 370.

Wickramasingha, Ishan Sobhy, Michael and Sherif, Sherif S. 2017. Bayesian Inference.

Tam, Gary K. L. Kothari, Vivek and Chen, Min 2017. An Analysis of Machine- and Human-Analytics in Classification. IEEE Transactions on Visualization and Computer Graphics, Vol. 23, Issue. 1, p. 71.

Elassal, Nada and Elder, James H. 2017. Computer Vision – ACCV 2016. Vol. 10115, Issue. , p. 329.

Wang, Zhehui Liu, Q. Waganaar, W. Fontanese, J. James, D. and Munsat, T. 2016. Four-dimensional (4D) tracking of high-temperature microparticles. Review of Scientific Instruments, Vol. 87, Issue. 11, p. 11D601.

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This document identifies white papers about TensorFlow.

Large-Scale Machine Learning on Heterogeneous Distributed Systems

Access this white paper.

Abstract: TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at

In BibTeX format

If you use TensorFlow in your research and would like to cite the TensorFlow system, we suggest you cite this whitepaper.

@misc{tensorflow2015-whitepaper, title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems}, url={}, note={Software available from}, author={ Mart\'{\i}n~Abadi and Ashish~Agarwal and Paul~Barham and Eugene~Brevdo and Zhifeng~Chen and Craig~Citro and Greg~S.~Corrado and Andy~Davis and Jeffrey~Dean and Matthieu~Devin and Sanjay~Ghemawat and Ian~Goodfellow and Andrew~Harp and Geoffrey~Irving and Michael~Isard and Yangqing Jia and Rafal~Jozefowicz and Lukasz~Kaiser and Manjunath~Kudlur and Josh~Levenberg and Dandelion~Man\'{e} and Rajat~Monga and Sherry~Moore and Derek~Murray and Chris~Olah and Mike~Schuster and Jonathon~Shlens and Benoit~Steiner and Ilya~Sutskever and Kunal~Talwar and Paul~Tucker and Vincent~Vanhoucke and Vijay~Vasudevan and Fernanda~Vi\'{e}gas and Oriol~Vinyals and Pete~Warden and Martin~Wattenberg and Martin~Wicke and Yuan~Yu and Xiaoqiang~Zheng}, year={2015}, }

Or in textual form:

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from

TensorFlow: A System for Large-Scale Machine Learning

Access this white paper.

Abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, generalpurpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

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