Amy McGovern
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Automatic discovery of subgoals in reinforcement learning using diverse density
A McGovern, AG Barto
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
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
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
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
Roles of macro-actions in accelerating reinforcement learning
A McGovern, RS Sutton, AH Fagg
Grace Hopper celebration of women in computing 1317, 15, 1997
Autonomous discovery of temporal abstractions from interaction with an environment
EA Mcgovern
University of Massachusetts Amherst, 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
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
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
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
Macro-actions in reinforcement learning: An empirical analysis
A McGovern, RS Sutton
Computer Science Department Faculty Publication Series, 15, 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
Classification of convective areas using decision trees
DJ Gagne, A McGovern, J Brotzge
Journal of Atmospheric and Oceanic Technology 26 (7), 1341-1353, 2009
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
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 102 (5), E1016-E1032, 2021
Building a basic block instruction scheduler with reinforcement learning and rollouts
A McGovern, E Moss, AG Barto
Machine learning 49, 141-160, 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
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
Calibration of machine learning–based probabilistic hail predictions for operational forecasting
A Burke, N Snook, DJ Gagne II, S McCorkle, A McGovern
Weather and Forecasting 35 (1), 149-168, 2020
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