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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks.

E. Racah, S. Ko, P. Sadowski, W. Bhimji, C. Tull, S. Oh, P. Baldi, and Prabhat. ICMLA, page 892-897. IEEE Computer Society, (2016)

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Regina Sangster University of Stuttgart

Bruno Ohngemach University of Stuttgart

Kathrin Ohmer University of Stuttgart

Viola Sänger University of Stuttgart

Sophie Ohnmacht University of Stuttgart

Other publications of authors with the same name

Extending DRM Features to Distributed Environments.S. Sim, Y. Oh, and S. Lee. Journal of Multimedia, 1 (5): 36-41 (2006)Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks.E. Racah, S. Ko, P. Sadowski, W. Bhimji, C. Tull, S. Oh, P. Baldi, and Prabhat. ICMLA, page 892-897. IEEE Computer Society, (2016)Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation.P. Koanantakool, A. Ali, A. Azad, A. Buluç, D. Morozov, L. Oliker, K. Yelick, and S. Oh. AISTATS, volume 84 of Proceedings of Machine Learning Research, page 1376-1386. PMLR, (2018)A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data.K. Khare, S. Oh, S. Rahman, and B. Rajaratnam. Machine Learning, 108 (12): 2061-2086 (2019)Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication.P. Koanantakool, A. Azad, A. Buluç, D. Morozov, S. Oh, L. Oliker, and K. Yelick. IPDPS, page 842-853. IEEE Computer Society, (2016)Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection.S. Oh, O. Dalal, K. Khare, and B. Rajaratnam. CoRR, (2014)Distributionally Robust Formulation and Model Selection for the Graphical Lasso.P. Cisneros-Velarde, S. Oh, and A. Petersen. CoRR, (2019)Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning.P. Koanantakool, A. Ali, A. Azad, A. Buluç, D. Morozov, S. Oh, L. Oliker, and K. Yelick. CoRR, (2017)Generalized Pseudolikelihood Methods for Inverse Covariance Estimation.A. Ali, K. Khare, S. Oh, and B. Rajaratnam. AISTATS, volume 54 of Proceedings of Machine Learning Research, page 280-288. PMLR, (2017)

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Disambiguation of "Oh, Sang-Yun"

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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks.

Please choose a person to relate this publication to

Regina Sangster University of Stuttgart

Bruno Ohngemach University of Stuttgart

Kathrin Ohmer University of Stuttgart

Viola Sänger University of Stuttgart

Sophie Ohnmacht University of Stuttgart

Other publications of authors with the same name

Disambiguation

PUMA

Disambiguation of "Oh, Sang-Yun"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks.

Please choose a person to relate this publication to

Regina Sangster University of Stuttgart

Bruno Ohngemach University of Stuttgart

Kathrin Ohmer University of Stuttgart

Viola Sänger University of Stuttgart

Sophie Ohnmacht University of Stuttgart

Other publications of authors with the same name

Disambiguation

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URL
DOI
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APA
Chicago
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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks.