Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science. - Antonios Mamalakis; Imme Ebert-Uphoff; Elizabeth A. Barnes (xxAI@ICML, 2020)

Rotated spectral principal component analysis (rsPCA) for identifying dynamical modes of variability in climate systems. - Clément Guilloteau; Antonios Mamalakis; Lawrence Vulis; Tryphon T. Georgiou; Efi Foufoula-Georgiou (CoRR, 2020)

Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset. - Antonios Mamalakis; Imme Ebert-Uphoff; Elizabeth A. Barnes (CoRR, 2021)

Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience. - Antonios Mamalakis; Elizabeth A. Barnes; Imme Ebert-Uphoff (CoRR, 2022)

Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience. - Antonios Mamalakis; Elizabeth A. Barnes; Imme Ebert-Uphoff (CoRR, 2022)

Solving the enigma: Deriving optimal explanations of deep networks. - Michail Mamalakis; Antonios Mamalakis; Ingrid Agartz; Lynn Egeland Mørch-Johnsen; Graham K. Murray; John Suckling; Pietro Lio (CoRR, 2024)

Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks. - Michail Mamalakis; Antonios Mamalakis; Ingrid Agartz; Lynn Egeland Mørch-Johnsen; Graham K. Murray; John Suckling; Pietro Lio (AI Open, 2025)