Neural Network Attribution Methods for Problems in Geoscience: A Novel
Synthetic Benchmark Dataset

Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset

18 March 2021

Antonios Mamalakis

I. Ebert‐Uphoff

Papers citing "Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset"

16 / 16 papers shown

Title
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves Alessandro Lovo Amaury Lancelin Corentin Herbert Freddy Bouchet AI4CE 23 0 0 01 Oct 2024
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators Ankur Mahesh William D. Collins Boris Bonev Noah D. Brenowitz Y. Cohen ... Michael S. Pritchard David Pruitt Mark Risser Shashank Subramanian Jared Willard 73 4 0 02 Aug 2024
Applications of Explainable artificial intelligence in Earth system science Feini Huang Shijie Jiang Lu Li Yongkun Zhang Ye Zhang Ruqing Zhang Qingliang Li Danxi Li Wei Shangguan Yongjiu Dai 30 2 0 12 Jun 2024
The Explanation Necessity for Healthcare AI Michail Mamalakis Héloïse de Vareilles Graham K Murray Pietro Lio' J. Suckling 33 2 0 31 May 2024
Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks Michail Mamalakis Antonios Mamalakis Ingrid Agartz L. Morch-Johnsen Graham K Murray J. Suckling Pietro Lio' 48 0 0 16 May 2024
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition Michail Mamalakis Héloïse de Vareilles Atheer AI-Manea Samantha C. Mitchell Ingrid Arartz ... Jane R. Garrison Jon S. Simons Pietro Lio' J. Suckling Graham K Murray 8 1 0 02 Sep 2023
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science P. Bommer M. Kretschmer Anna Hedström Dilyara Bareeva Marina M.-C. Höhne 29 38 0 01 Mar 2023
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction Montgomery Flora Corey K. Potvin A. McGovern Shawn Handler FAtt 13 4 0 18 Nov 2022
Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement Montgomery Flora Corey K. Potvin A. McGovern Shawn Handler FAtt 11 16 0 16 Nov 2022
Lazy Estimation of Variable Importance for Large Neural Networks Yue Gao Abby Stevens Rebecca Willett Garvesh Raskutti 27 4 0 19 Jul 2022
Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review S. Salcedo-Sanz Jorge Pérez-Aracil G. Ascenso Javier Del Ser D. Casillas-Pérez ... D. Barriopedro R. García-Herrera Marcello Restelli M. Giuliani A. Castelletti AI4Cl 15 13 0 03 Jun 2022
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics Mariana C. A. Clare Maike Sonnewald Redouane Lguensat Julie Deshayes Venkatramani Balaji BDL 13 31 0 30 Apr 2022
Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data A. H. Nielsen Alexandros Iosifidis H. Karstoft AI4Cl AI4CE 13 6 0 10 Feb 2022
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience Antonios Mamalakis E. Barnes I. Ebert‐Uphoff 19 73 0 07 Feb 2022
Controlled abstention neural networks for identifying skillful predictions for classification problems E. Barnes R. Barnes 13 8 0 16 Apr 2021
Controlled abstention neural networks for identifying skillful predictions for regression problems E. Barnes R. Barnes 23 22 0 16 Apr 2021