| Automatic discovery of subgoals in reinforcement learning using diverse density A McGovern, AG Barto | 644 | 2001 |
| Using artificial intelligence to improve real-time decision-making for high-impact weather A McGovern, KL Elmore, DJ Gagne, SE Haupt, CD Karstens, R Lagerquist, ... Bulletin of the American Meteorological Society 98 (10), 2073-2090, 2017 | 400 | 2017 |
| Making the black box more transparent: Understanding the physical implications of machine learning A McGovern, R Lagerquist, DJ Gagne, GE Jergensen, KL Elmore, ... Bulletin of the American Meteorological Society 100 (11), 2175-2199, 2019 | 398 | 2019 |
| Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles DJ Gagne, A McGovern, SE Haupt, RA Sobash, JK Williams, M Xue Weather and forecasting 32 (5), 1819-1840, 2017 | 173 | 2017 |
| Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction A McGovern, DH Rosendahl, RA Brown, KK Droegemeier Data Mining and Knowledge Discovery 22, 232-258, 2011 | 158 | 2011 |
| Roles of macro-actions in accelerating reinforcement learning A McGovern, RS Sutton, AH Fagg Grace Hopper celebration of women in computing 1317, 15, 1997 | 138 | 1997 |
| Autonomous discovery of temporal abstractions from interaction with an environment EA Mcgovern University of Massachusetts Amherst, 2002 | 125 | 2002 |
| Deep learning for spatially explicit prediction of synoptic-scale fronts R Lagerquist, A McGovern, DJ Gagne II Weather and Forecasting 34 (4), 1137-1160, 2019 | 98 | 2019 |
| Machine learning for real-time prediction of damaging straight-line convective wind R Lagerquist, A McGovern, T Smith Weather and Forecasting 32 (6), 2175-2193, 2017 | 96 | 2017 |
| Deep learning on three-dimensional multiscale data for next-hour tornado prediction R Lagerquist, A McGovern, CR Homeyer, DJ Gagne II, T Smith Monthly Weather Review 148 (7), 2837-2861, 2020 | 93 | 2020 |
| Machine learning enhancement of storm-scale ensemble probabilistic quantitative precipitation forecasts DJ Gagne, A McGovern, M Xue Weather and Forecasting 29 (4), 1024-1043, 2014 | 84 | 2014 |
| Macro-actions in reinforcement learning: An empirical analysis A McGovern, RS Sutton Computer Science Department Faculty Publication Series, 15, 1998 | 83 | 1998 |
| Exploiting relational structure to understand publication patterns in high-energy physics A McGovern, L Friedland, M Hay, B Gallagher, A Fast, J Neville, D Jensen Acm Sigkdd Explorations Newsletter 5 (2), 165-172, 2003 | 73 | 2003 |
| Classification of convective areas using decision trees DJ Gagne, A McGovern, J Brotzge Journal of Atmospheric and Oceanic Technology 26 (7), 1341-1353, 2009 | 71 | 2009 |
| Outlook for exploiting artificial intelligence in the earth and environmental sciences SA Boukabara, V Krasnopolsky, SG Penny, JQ Stewart, A McGovern, ... Bulletin of the American Meteorological Society, 1-53, 2020 | 62 | 2020 |
| Building a basic block instruction scheduler with reinforcement learning and rollouts A McGovern, E Moss, AG Barto Machine learning 49, 141-160, 2002 | 62 | 2002 |
| Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning A McGovern, DJ Gagne, JK Williams, RA Brown, JB Basara Machine learning 95, 27-50, 2014 | 60 | 2014 |
| Evaluating knowledge to support climate action: A framework for sustained assessment. Report of an independent advisory committee on applied climate assessment RH Moss, S Avery, K Baja, M Burkett, AM Chischilly, J Dell, PA Fleming, ... Weather, Climate, and Society 11 (3), 465-487, 2019 | 58 | 2019 |
| Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science A McGovern, I Ebert-Uphoff, DJ Gagne, A Bostrom Environmental Data Science 1, e6, 2022 | 57 | 2022 |
| Scheduling straight-line code using reinforcement learning and rollouts A McGovern, J Moss Advances in neural information processing Systems 11, 1998 | 57 | 1998 |
David John Gagne IINational Center for Atmospheric Research在 ucar.edu 的电子邮件经过验证
