Search Results for author: C.

Found 3 papers, 2 papers with code

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

no code implementations12 Nov 2020 Botelho, Joshi, A., Khara, Sarkar, S., Hegde, C., Rao, V., Adavani, S.S., & Ganapathysubramanian, B.

Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models training in reasonable time as well as distributing the storage requirements.

Distributed Computing

Cosmic Background Removal with Deep Neural Networks in SBND

1 code implementation2 Dec 2020 SBND Collaboration, R. Acciarri, C. Adams, C. Andreopoulos, J. Asaadi, M. Babicz, C. Backhouse, W. Badgett, L. Bagby, D. Barker, V. Basque, Q. Bazetto, M. Betancourt, A. Bhanderi, A. Bhat, C. Bonifazi, D. Brailsford, G. Brandt, T. Brooks, F. Carneiro, Y. Chen, H. Chen, G. Chisnall, I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I., L. de Icaza Astiz, A. De Roeck, G. de Sá Pereira, M. Del Tutto, V. Di Benedetto, A. Ereditato, J. Evans, C. Ezeribe, S. Fitzpatrick, T. Fleming, W. Foreman, D. Franco, I. Furic, P. Furmanski, S. Gao, D. Garcia-Gamez, H. Frandini, G. Ge, I. Gil-Botella, S. Gollapinni, O. Goodwin, P. Green, C. Griffith, R. Guenette, P. Guzowski, T. Ham, J. Henzerling, A. Holin, B. Howard, R., S. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M., J. Kim, A. Kudryavtsev, J. Larkin, H. Lay, I. Lepetic, B., R. Littlejohn, W., C. Louis, A., A. Machado, M. Malek, D. Mardsen, C. Mariani, F. Marinho, A. Mastbaum, K. Mavrokoridis, N. McConkey, V. Meddage, P. Méndez, T. Mettler, K. Mistry, A. Mogan, J. Molina, M. Mooney, L. Mora, C., A. Moura, J. Mousseau, A. Navrer-Agasson, F., J. Nicolas-Arnaldos, A. Nowak, O. Palamara, V. Pandey, J. Pater, L. Paulucci, V., L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. Rivera, M. Roda, M. Ross-Lonergan, G. Scanavini, A. Scarff, D., W. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Soderberg, S. Söldner-Rembold, J. Spitz, N., J., C. Spooner, M. Stancari, V. Stenico, A. Szelc, W. Tang, J. Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G., A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. Yu, B. Zamorano, J. Zennamo, A. Zglam

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded.

Semantic Segmentation Data Analysis, Statistics and Probability

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