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  • 6 Similar Profiles

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Research Output

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

Allen, G., Andreoni, I., Bachelet, E., Berriman, G. B., Bianco, F. B., Biswas, R., Kind, M. C., Chard, K., Cho, M., Cowperthwaite, P. S., Etienne, Z. B., George, D., Gibbs, T., Graham, M., Gropp, W., Gupta, A., Haas, R., Huerta, E. A., Jennings, E., Katz, D. S. & 28 others, Khan, A., Kindratenko, V., Kramer, W. T. C., Liu, X., Mahabal, A., McHenry, K., Miller, J. M., Neubauer, M. S., Oberlin, S., Jr, A. R. O., Rosofsky, S., Ruiz, M., Saxton, A., Schutz, B., Schwing, A., Seidel, E., Shapiro, S. L., Shen, H., Shen, Y., Sipőcz, B. M., Sun, L., Towns, J., Tsokaros, A., Wei, W., Wells, J., Williams, T. J., Xiong, J. & Zhao, Z., Feb 1 2019, 15 p.

Research output: Book/Report/Conference proceedingCommissioned report

  • Enabling real-time multi-messenger astrophysics discoveries with deep learning

    Huerta, E. A., Allen, G., Andreoni, I., Antelis, J. M., Bachelet, E., Berriman, G. B., Bianco, F. B., Biswas, R., Carrasco kind, M., Chard, K., Cho, M., Cowperthwaite, P. S., Etienne, Z. B., Fishbach, M., Forster, F., George, D., Gibbs, T., Graham, M., Gropp, W., Gruendl, R. & 40 others, Gupta, A., Haas, R., Habib, S., Jennings, E., Johnson, M. W. G., Katsavounidis, E., Katz, D. S., Khan, A., Kindratenko, V., Kramer, W. T. C., Liu, X., Mahabal, A., Marka, Z., Mchenry, K., Miller, J. M., Moreno, C., Neubauer, M. S., Oberlin, S., Olivas, A. R., Petravick, D., Rebei, A., Rosofsky, S., Ruiz, M., Saxton, A., Schutz, B. F., Schwing, A., Seidel, E., Shapiro, S. L., Shen, H., Shen, Y., Singer, L. P., Sipocz, B. M., Sun, L., Towns, J., Tsokaros, A., Wei, W., Wells, J., Williams, T. J., Xiong, J. & Zhao, Z., Oct 1 2019, In : Nature Reviews Physics. 1, 10, p. 600-608 9 p.

    Research output: Contribution to journalArticle

  • Tensorflow-tracing: A performance tuning framework for production

    Hashemi, S. H., Rausch, P., Rabe, B., Chou, K. Y., Liu, S., Kindratenko, V. & Campbell, R. H., Jan 1 2019, Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019. USENIX Association, p. 31-33 3 p. (Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Numerical computations with GPUs

    Kindratenko, V., Jan 1 2014, Springer International Publishing. 405 p.

    Research output: Book/Report/Conference proceedingBook

  • Modern accelerator technologies for geographic information science

    Shi, X., Kindratenko, V. & Yang, C., Aug 1 2013, Modern accelerator technologies for geographic information science. Springer US, Vol. 9781461487456. p. 3-6 4 p.

    Research output: Chapter in Book/Report/Conference proceedingForeword/postscript