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Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms.
Benchmarking attribution methods with relative feature importance
M. Yang and B. Kim · 1907
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Notes on the n-person game—ii: The value of an n-person game.(1951)
L. S. Shapley · 1951
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Forecast evaluation
A. H. Murphy and H. Daan · 1985
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Skill scores based on the mean square error and their relationships to the correlation coefficient
A. H. Murphy · 1988
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Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
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Comparing measures of sparsity, 2009
N. Hurley and S. Rickard · 2009
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How to explain individual classification decisions
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. Müller · 2010
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An efficient explanation of individual classifications using game theory
E. Strumbelj and I. Kononenko · 2010
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The community earth system model: A framework for collaborative research
J. W. Hurrell, M. M. Holland, P. R. Gent, S. Ghan, J. E. Kay, P. J. Kushner, J.-F. Lamarque, W. G. Large, D. Lawrence, K. Lindsay, et al · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
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The community earth system model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability
J. E. Kay, C. Deser, A. Phillips, A. Mai, C. Hannay, G. Strand, J. M. Arblaster, S. C. Bates, G. Danabasoglu, J. Edwards, M. Holland, P. Kushner, J.-F. Lamarque, D. Lawrence, K. Lindsay, A. Middleton, E. Munoz, R. Neale, K. Oleson, L. Polvani, and M. Vertenstein · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo · 2015
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Opening the black box: Revealing interpretable sequence motifs in kernel-based learning algorithms
M. M.-C. Vidovic, N. Görnitz, K.-R. Müller, G. Rätsch, and M. Kloft · 2015
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Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
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”why should i trust you?”: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Evaluating the visualization of what a deep neural network has learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.-R. Muller · 2016
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Not just a black box: Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, A. Shcherbina, and A. Kundaje · 2016
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Feature importance measure for non-linear learning algorithms
M. M.-C. Vidovic, N. Görnitz, K.-R. Müller, and M. Kloft · 2016
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The shattered gradients problem: If resnets are the answer, then what is the question?
D. Balduzzi, M. Frean, L. Leary, J. Lewis, K. W.-D. Ma, and B. McWilliams · 2017
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A machine learning nowcasting method based on real-time reanalysis data
L. Han, J. Sun, W. Zhang, Y. Xiu, H. Feng, and Y. Lin · 2017
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SoilGrids250m: Global gridded soil information based on machine learning
T. Hengl, J. Mendes de Jesus, G. B. Heuvelink, M. Ruiperez Gonzalez, M. Kilibarda, A. Blagotić, W. Shangguan, M. N. Wright, X. Geng, B. Bauer-Marschallinger, et al · 2017
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A Unified Approach to Interpreting Model Predictions
S. M. Lundberg and S.-I. Lee · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller · 2017
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Methods for interpreting and understanding deep neural networks
G. Montavon, W. Samek, and K.-R. Müller · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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Towards robust interpretability with self-explaining neural networks
D. Alvarez Melis and T. Jaakkola · 2018
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On the robustness of interpretability methods
D. Alvarez-Melis and T. S. Jaakkola · 2018
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Metrics for explainable ai: Challenges and prospects
R. R. Hoffman, S. T. Mueller, G. Klein, and J. Litman · 2018
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Rise: Randomized input sampling for explanation of black-box models
V. Petsiuk, A. Das, and K. Saenko · 2018
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Top-down neural attention by excitation backprop
J. Zhang, S. A. Bargal, Z. Lin, J. Brandt, X. Shen, and S. Sclaroff · 2018
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iNNvestigate neural networks!
M. Alber, S. Lapuschkin, P. Seegerer, M. Hägele, K. T. Schütt, G. Montavon, W. Samek, K.-R. Müller, S. Dähne, and P.-J. Kindermans · 2019
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A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
N. Anantrasirichai, J. Biggs, F. Albino, and D. Bull · 2019
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Gradient-based attribution methods
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2019
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, et al · 2019
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Explaining by removing: A unified framework for model explanation
I. C. Covert, S. Lundberg, and S.-I. Lee · 2021
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Interpretable and explainable ai (xai) model for spatial drought prediction
A. Dikshit and B. Pradhan · 2021
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Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
E. Felsche and R. Ludwig · 2021
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Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
P. B. Gibson, W. E. Chapman, A. Altinok, L. Delle Monache, M. J. DeFlorio, and D. E. Waliser · 2021
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Emulating aerosol microphysics with machine learning
P. Harder, D. Watson-Parris, D. Strassel, N. Gauger, P. Stier, and J. Keuper · 2021
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C. L. Bromberg, C. Gazen, J. J. Hickey, J. Burge, L. Barrington, and S. Agrawal · 2019
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This looks like that: deep learning for interpretable image recognition
C. Chen, O. Li, D. Tao, A. Barnett, C. Rudin, and J. K. Su · 2019
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Deep learning for multi-year enso forecasts
Y.-G. Ham, J.-H. Kim, and J.-J. Luo · 2019
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Making the black box more transparent: Understanding the physical implications of machine learning
A. McGovern, R. Lagerquist, D. John Gagne, G. E. Jergensen, K. L. Elmore, C. R. Homeyer, and T. Smith · 2019
Cited alongside, same era.
Layer-wise relevance propagation: an overview
G. Montavon, A. Binder, S. Lapuschkin, W. Samek, and K.-R. Müller · 2019
Cited alongside, same era.
Explainable AI: interpreting, explaining and visualizing deep learning , volume 11700
W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K.-R. Müller · 2019
Cited alongside, same era.
Towards a more reliable historical reanalysis: Improvements for version 3 of the twentieth century reanalysis system
L. C. Slivinski, G. P. Compo, J. S. Whitaker, P. D. Sardeshmukh, B. S. Giese, C. McColl, R. Allan, X. Yin, R. Vose, H. Titchner, J. Kennedy, L. J. Spencer, L. Ashcroft, S. Brönnimann, M. Brunet, D. Camuffo, R. Cornes, T. A. Cram, R. Crouthamel, F. Domínguez-Castro, J. E. Freeman, J. Gergis, E. Hawkins, P. D. Jones, S. Jourdain, A. Kaplan, H. Kubota, F. L. Blancq, T.-C. Lee, A. Lorrey, J. Luterbacher, M. Maugeri, C. J. Mock, G. K. Moore, R. Przybylak, C. Pudmenzky, C. Reason, V. C. Slonosky, C. A. Smith, B. Tinz, B. Trewin, M. A. Valente, X. L. Wang, C. Wilkinson, K. Wood, and P. Wyszyński · 2019
Cited alongside, same era.
Sub-seasonal climate forecasting via machine learning: Challenges, analysis, and advances
S. He, X. Li, T. DelSole, P. Ravikumar, and A. Banerjee · 2021
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Development and interpretation of a neural-network-based synthetic radar reflectivity estimator using goes-r satellite observations
K. A. Hilburn, I. Ebert-Uphoff, and S. D. Miller · 2021
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Detecting climate signals using explainable AI with single-forcing large ensembles
Z. M. Labe and E. A. Barnes · 2021
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Subseasonal forecasts of opportunity identified by an explainable neural network
K. J. Mayer and E. A. Barnes · 2021
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A multidisciplinary survey and framework for design and evaluation of explainable ai systems
S. Mohseni, N. Zarei, and E. D. Ragan · 2021
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Ensemble methods for neural network-based weather forecasts
S. Scher and G. Messori · 2021
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Revealing the impact of global heating on north atlantic circulation using transparent machine learning
M. Sonnewald and R. Lguensat · 2021
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Multisensor remote sensing imagery super-resolution with conditional gan
J. Wang, K. Gao, Z. Zhang, C. Ni, Z. Hu, D. Chen, and Q. Wu · 2021
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OpenXAI: Towards a transparent evaluation of model explanations
C. Agarwal, S. Krishna, E. Saxena, M. Pawelczyk, N. Johnson, I. Puri, M. Zitnik, and H. Lakkaraju · 2022
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Focus! rating xai methods and finding biases
A. Arias-Duart, F. Parés, D. Garcia-Gasulla, and V. Giménez-Ábalos · 2022
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Evaluation of interpretability methods and perturbation artifacts in deep neural networks
L. Brocki and N. C. Chung · 2022
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Explainable artificial intelligence for bayesian neural networks: toward trustworthy predictions of ocean dynamics
M. C. Clare, M. Sonnewald, R. Lguensat, J. Deshayes, and V. Balaji · 2022
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Protovae: A trustworthy self-explainable prototypical variational model
S. Gautam, A. Boubekki, S. Hansen, S. Salahuddin, R. Jenssen, M. Höhne, and M. Kampffmeyer · 2022
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Evaluating feature attribution methods in the image domain
A. Gevaert, A.-J. Rousseau, T. Becker, D. Valkenborg, T. De Bie, and Y. Saeys · 2022
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Visualizing the diversity of representations learned by bayesian neural networks
D. Grinwald, K. Bykov, S. Nakajima, and M. M.-C. Höhne · 2022
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T. Han, S. Srinivas, and H. Lakkaraju · 2022
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The disagreement problem in explainable machine learning: A practitioner’s perspective
S. Krishna, T. Han, A. Gu, J. Pombra, S. Jabbari, S. Wu, and H. Lakkaraju · 2022
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Comparison of climate model large ensembles with observations in the arctic using simple neural networks
Z. M. Labe and E. A. Barnes · 2022
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Toward explainable artificial intelligence for regression models: A methodological perspective
S. Letzgus, P. Wagner, J. Lederer, W. Samek, K.-R. Müller, and G. Montavon · 2022
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Understanding predictability of daily southeast u.s. precipitation using explainable machine learning
K. Pegion, E. J. Becker, and B. P. Kirtman · 2022
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C-senn: Contrastive self-explaining neural network
Y. Sawada and K. Nakamura · 2022
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Interpretable semantic photo geolocation
J. Theiner, E. Müller-Budack, and R. Ewerth · 2022
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Using explainable machine learning forecasts to discover subseasonal drivers of high summer temperatures in western and central europe
C. Van Straaten, K. Whan, D. Coumou, B. Van den Hurk, and M. Schmeits · 2022
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Do feature attribution methods correctly attribute features?
Y. Zhou, S. Booth, M. T. Ribeiro, and J. Shah · 2022
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This looks more like that: Enhancing self-explaining models by prototypical relevance propagation
S. Gautam, M. M.-C. Höhne, S. Hansen, R. Jenssen, and M. Kampffmeyer · 2023
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Understanding spatial context in convolutional neural networks using explainable methods: Application to interpretable GREMLIN
K. A. Hilburn · 2023
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A machine learning explainability tutorial for atmospheric sciences
M. L. Flora, C. K. Potvin, A. McGovern, and S. Handler · 2024
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Unmasking clever hans predictors and assessing what machines really learn
S. Lapuschkin, S. Wäldchen, A. Binder, G. Montavon, W. Samek, and K.-R. Müller · 2041
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