| 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 | 393 | 2016 |
| Unsupervised state representation learning in atari A Anand*, E Racah*, S Ozair*, Y Bengio, MA Côté, RD Hjelm NeurIPS 2019, 2019 | 274 | 2019 |
| 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 NeurIPS 2017, 2017 | 271 | 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 | 95 | 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 | 86 | 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, 2017 | 57 | 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, L Gerhardt, S Byna, ... Cray user group, 2016 | 57* | 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 | 44 | 2016 |
| Hierarchical model-based imitation learning for planning in autonomous driving E Bronstein, M Palatucci, D Notz, B White, A Kuefler, Y Lu, S Paul, ... 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 41 | 2022 |
| 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 | 41 | 2016 |
| The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning H van Seijen, H Nekoei, E Racah, S Chandar Advances in Neural Information Processing Systems, 2020 | 14 | 2020 |
| 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 | 9 | 2016 |
| Slot Contrastive Networks: A Contrastive Approach for Representing Objects E Racah, S Chandar ICML 2020 Workshop on Object-Oriented Learning, 2020 | 8 | 2020 |
| Deep learning for detecting extreme weather patterns M Mudigonda, P Ram, K Kashinath, E Racah, A Mahesh, Y Liu, ... Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021 | 6 | 2021 |
| Toward interactive supercomputing at NERSC with Jupyter R Thomas, S Canon, S Cholia, L Gerhardt, E Racah Cray User Group (CUG) Conference Proceedings, 2017 | 5 | 2017 |
| Automated detection of fronts using a deep learning algorithm KE Kunkel, JC Biard, E Racah 98th American Meteorological Society Annual Meeting, 2018 | 3 | 2018 |
| Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments E Racah, C Pal ICML Workshop on Self-Supervised Learning 2019, 2019 | 1 | 2019 |
| Characterizing the Performance of Analytics Workloads on the Cray XC40 M Ringenburg, S Zhang, K Maschhoff, B Sparks, E Racah Cray User Group (CUG) meeting 5, 2016 | 1 | 2016 |
| Unsupervised representation learning in interactive environments E Racah Master's Thesis, UdeM, 2020 | | 2020 |
| Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics in Spark and C+ MPI A Gittens, A Devarakonda, E Racah | | 2016 |
Wahid BhimjiLawrence Berkeley National Laboratory在 lbl.gov 的电子邮件经过验证
