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PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.

M. Patwary, N. Satish, N. Sundaram, J. Liu, P. Sadowski, E. Racah, S. Byna, C. Tull, W. Bhimji, Prabhat, and P. Dubey. IPDPS, page 494-503. IEEE Computer Society, (2016)

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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.A. Baydin, L. Shao, W. Bhimji, L. Heinrich, S. Naderiparizi, A. Munk, J. Liu, B. Gram-Hansen, G. Louppe, L. Meadows and 5 other author(s). NeurIPS, page 5460-5473. (2019)PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.M. Patwary, N. Satish, N. Sundaram, J. Liu, P. Sadowski, E. Racah, S. Byna, C. Tull, W. Bhimji, Prabhat and 1 other author(s). IPDPS, page 494-503. IEEE Computer Society, (2016)Machine Learning in High Energy Physics Community White Paper.K. Albertsson, P. Altoe, D. Anderson, M. Andrews, J. Espinosa, A. Aurisano, L. Basara, A. Bevan, W. Bhimji, D. Bonacorsi and 108 other author(s). CoRR, (2018)Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC.W. Bhimji, S. Farrell, T. Kurth, M. Paganini, Prabhat, and E. Racah. CoRR, (2017)Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.M. Casado, A. Baydin, D. Martínez-Rubio, T. Le, F. Wood, L. Heinrich, G. Louppe, K. Cranmer, K. Ng, W. Bhimji and 1 other author(s). CoRR, (2017)Interactive Distributed Deep Learning with Jupyter Notebooks.S. Farrell, A. Vose, O. Evans, M. Henderson, S. Cholia, F. Pérez, W. Bhimji, S. Canon, R. Thomas, and Prabhat. ISC Workshops, volume 11203 of Lecture Notes in Computer Science, page 678-687. Springer, (2018)Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.A. Baydin, L. Heinrich, W. Bhimji, B. Gram-Hansen, G. Louppe, L. Shao, Prabhat, K. Cranmer, and F. Wood. CoRR, (2018)Graph Neural Networks for IceCube Signal Classification.N. Choma, F. Monti, L. Gerhardt, T. Palczewski, Z. Ronaghi, Prabhat, W. Bhimji, M. Bronstein, S. Klein, and J. Bruna. ICMLA, page 386-391. IEEE, (2018)Deep learning at 15PF: supervised and semi-supervised classification for scientific data.T. Kurth, J. Zhang, N. Satish, E. Racah, I. Mitliagkas, M. Patwary, T. Malas, N. Sundaram, W. Bhimji, M. Smorkalov and 5 other author(s). SC, page 7:1-7:11. ACM, (2017)Creating Virtual Universes Using Generative Adversarial Networks.M. Mustafa, D. Bard, W. Bhimji, R. Al-Rfou', and Z. Lukic. CoRR, (2017)

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PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.

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PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.