112 citations · 295 across the 7 of their papers we have counts for
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cs.LG2022★ 11 cited
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction
Montgomery Flora, Corey Potvin, Amy McGovern +1
Machine learning (ML) models are becoming increasingly common in the atmospheric science community with a wide range of applications. To enable users to understand what an ML model…
cs.LG2022★ 34 cited
A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning
Randy J. Chase, David R. Harrison, Gary Lackmann +1
Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order…