Evan Racah
TitleCited byYear
Application of deep convolutional neural networks for detecting extreme weather in climate datasets
Y Liu, E Racah, J Correa, A Khosrowshahi, D Lavers, K Kunkel, ...
arXiv preprint arXiv:1605.01156, 2016
Matrix factorizations at scale: A comparison of scientific data analytics in Spark and C+ MPI using three case studies
A Gittens, A Devarakonda, E Racah, M Ringenburg, L Gerhardt, ...
2016 IEEE International Conference on Big Data (Big Data), 204-213, 2016
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
E Racah, C Beckham, T Maharaj, SE Kahou, M Prabhat, C Pal
Advances in Neural Information Processing Systems, 3402-3413, 2017
Deep learning at 15pf: supervised and semi-supervised classification for scientific data
T Kurth, J Zhang, N Satish, E Racah, I Mitliagkas, MMA Patwary, T Malas, ...
Proceedings of the International Conference for High Performance Computing …, 2017
H5spark: bridging the I/O gap between spark and scientific data formats on Hpc systems
J Liu, E Racah, Q Koziol, RS Canon, A Gittens
Cray user group, 2016
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
W Bhimji, SA Farrell, T Kurth, M Paganini, E Racah
Journal of Physics: Conference Series 1085 (4), 042034, 2018
Revealing fundamental physics from the Daya Bay Neutrino Experiment using deep neural networks
E Racah, S Ko, P Sadowski, W Bhimji, C Tull, SY Oh, P Baldi
2016 15th IEEE International Conference on Machine Learning and Applications …, 2016
PANDA: Extreme scale parallel k-nearest neighbor on distributed architectures
MMA Patwary, NR Satish, N Sundaram, J Liu, P Sadowski, E Racah, ...
2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2016
A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark
A Gittens, J Kottalam, J Yang, MF Ringenburg, J Chhugani, E Racah, ...
2016 IEEE International Parallel and Distributed Processing Symposium …, 2016
Unsupervised State Representation Learning in Atari
A Anand*, E Racah*, S Ozair*, Y Bengio, MA Côté, RD Hjelm
NeurIPS 2019, 2019
Deep Learning for Extreme Weather Detection
M Prabhat, E Racah, J Biard, Y Liu, M Mudigonda, K Kashinath, ...
AGU Fall Meeting Abstracts, 2017
Toward Interactive Supercomputing at NERSC with Jupyter
R Thomas, S Canon, S Cholia, L Gerhardt, E Racah
Cray User Group (CUG) Conference Proceedings, 2017
Characterizing the Performance of Analytics Workloads on the Cray XC40
MF Ringenburg, S Zhang, KJ Maschhoff, B Sparks, E Racah
Cray User Group (CUG) meeting 5, 2016
Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments
E Racah, C Pal
arXiv preprint arXiv:1906.11951, 2019
Deep learning with raw data from Daya Bay
S Kohn, E Racah, C Tull, W Bhimji, D Dwyer
J. Phys. Conf. Ser. 898, 072050, 2017
Data Intensive Supercomputing at NERSC
L Gerhardt, S Cholia, M Prabhat, J Correa, A Greiner, W Bhimji, D Bard, ...
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