| 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 | 100 | 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 | 50* | 2017 |
| 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 | 50 | 2016 |
| 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 | 43 | 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 | 25* | 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 | 24 | 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 | 22 | 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 | 13 | 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 | 6 | 2016 |
| Unsupervised State Representation Learning in Atari A Anand*, E Racah*, S Ozair*, Y Bengio, MA Côté, RD Hjelm NeurIPS 2019, 2019 | 2 | 2019 |
| Toward Interactive Supercomputing at NERSC with Jupyter R Thomas, S Canon, S Cholia, L Gerhardt, E Racah Cray User Group (CUG) Conference Proceedings, 2017 | 2 | 2017 |
| Automated detection of fronts using a deep learning algorithm KE Kunkel, JC Biard, E Racah 98th American Meteorological Society Annual Meeting, 2018 | 1 | 2018 |
| 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 | 1 | 2016 |
| Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments E Racah, C Pal ICML Workshop on Self-Supervised Learning 2019, 2019 | | 2019 |
| Deep Learning for Detecting Extreme Weather and Climate Patterns (Core Science Keynote) W Collins, M Prabhat, E Racah, Y Liu, K Kashinath, C Pal, JC Biard, ... 98th American Meteorological Society Annual Meeting, 2018 | | 2018 |
| Data Intensive Supercomputing at NERSC L Gerhardt, S Cholia, M Prabhat, J Correa, A Greiner, W Bhimji, D Bard, ... | | 2015 |
Chris PalProfessor, Polytechnique Montréal & Mila, Element AI, Canada CIFAR AI ChairVerified email at polymtl.ca
