Performance Benchmarking of Parallel Hyperparameter Tuning for Deep Learning Based Tornado Predictions
Authors:
- Jonathan N. BasalygaJBJonathan N. Basalyga
- Carlos A. BarajasCBCarlos A. Barajas
- Matthias K. GobbertMatthias K. Gobbert
- Jianwu WangJianwu Wang
- 1 July 2021
- journal article
- research article
- Published by Elsevier in Big Data Research
- Vol. 25
Abstract
No abstract availableKeywords
Funding Information
- National Science Foundation (0821258, 0821311, 1228778, 1726023, 1730250, CNS–0821258, CNS–1228778, DMS–0821311, OAC–1726023, OAC–1730250)
- University of Maryland, Baltimore County
- Quest High Performance Computing
This publication has 5 references indexed in Scilit:
- Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of PrecipitationAtmosphere, 2019
- Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact WeatherBulletin of the American Meteorological Society, 2017
- Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?Journal of Atmospheric and Oceanic Technology, 2015
- Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning AlgorithmsProceedings of the Python in Science Conference, 2013
- False Alarms and Close Calls: A Conceptual Model of Warning AccuracyWeather and Forecasting, 2007