| Metnet: A neural weather model for precipitation forecasting CK Sønderby, L Espeholt, J Heek, M Dehghani, A Oliver, T Salimans, ... arXiv preprint arXiv:2003.12140, 2020 | 309 | 2020 |
| Machine Learning for Precipitation Nowcasting from Radar Images S Agrawal, L Barrington, C Bromberg, J Burge, C Gazen, J Hickey Neural Information Processing Systems, 2019 | 264* | 2019 |
| Deep learning for twelve hour precipitation forecasts L Espeholt, S Agrawal, C Sønderby, M Kumar, J Heek, C Bromberg, ... Nature communications 13 (1), 1-10, 2022 | 171 | 2022 |
| Runtime verification of k-safety hyperproperties in HyperLTL S Agrawal, B Bonakdarpour 2016 IEEE 29th Computer Security Foundations Symposium (CSF), 239-252, 2016 | 82 | 2016 |
| WeatherBench 2: A benchmark for the next generation of data‐driven global weather models S Rasp, S Hoyer, A Merose, I Langmore, P Battaglia, T Russell, ... Journal of Advances in Modeling Earth Systems 16 (6), e2023MS004019, 2024 | 33 | 2024 |
| Global extreme heat forecasting using neural weather models I Lopez-Gomez, A McGovern, S Agrawal, J Hickey Artificial Intelligence for the Earth Systems 2 (1), 2023 | 31 | 2023 |
| Deep learning for day forecasts from sparse observations M Andrychowicz, L Espeholt, D Li, S Merchant, A Merose, F Zyda, ... arXiv preprint arXiv:2306.06079, 2023 | 26 | 2023 |
| A performance comparison of algorithms for byzantine agreement in distributed systems S Agrawal, K Daudjee 2016 12th European Dependable Computing Conference (EDCC), 249-260, 2016 | 16 | 2016 |
| Skillful twelve hour precipitation forecasts using large context neural networks. arXiv 2021 L Espeholt, S Agrawal, C Sønderby, M Kumar, J Heek, C Bromberg, ... arXiv preprint arXiv:2111.07470, 0 | 10 | |
| A machine learning outlook: Post-processing of global medium-range forecasts S Agrawal, R Carver, C Gazen, E Maddy, V Krasnopolsky, C Bromberg, ... arXiv preprint arXiv:2303.16301, 2023 | 3 | 2023 |
| The rain check M Sundararajan, S Agrawal | 1 | 2021 |
| Monitoring and enforcement of safety hyperproperties S Agrawal University of Waterloo, 2015 | 1 | 2015 |
| Withdrawn: Downscaling of Global Precipitation Forecasts with Probabilistic Generative Models RW Carver, C Gazen, S Agrawal, LL Li, F Sha 103rd AMS Annual Meeting, 2023 | | 2023 |
| Enhanced Deep Learning-Based Forecasting of Extreme Heat through Custom Exponential Losses I Lopez-Gomez, A McGovern, S Agrawal, J Hickey 103rd AMS Annual Meeting, 2023 | | 2023 |
| Global Extreme Heat Forecasting on Subseasonal Time Scales Using Deep Learning I Lopez-Gomez, A McGovern, S Agrawal, J Hickey 102nd American Meteorological Society Annual Meeting, 2022 | | 2022 |
| Quantifying and interpreting the value of precipitation input into data-driven models for river flood forecasting E Morin, C Gazen, G Shalev, GS Nearing, Z Moshe, O Reich, A Metzger, ... 101st American Meteorological Society Annual Meeting, 2021 | | 2021 |
| Getting the most out of satellite-based precipitation data for forecasting river floods with deep neural networks E Morin, C Gazen, Z Moshe, O Reich, A Metzger, G Shalev, G Begelman, ... AGU Fall Meeting Abstracts 2020, H191-02, 2020 | | 2020 |
| A Pure Deep Learning Approach to Precipitation Nowcasting J Hickey, C Gazen, S Agrawal, C Bromberg, L Barrington, V Lakshmanan, ... 100th American Meteorological Society Annual Meeting, 2020 | | 2020 |
| Complexity of Program Repair for Safety Hyperproperties BB Shreya Agrawal | | 2014 |
Jason HickeyGoogle Research在 google.com 的电子邮件经过验证
