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Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

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Shapley Explanation NetworksPreprint

Wang, R., Wang, X., & Inouye, D. I. (2021) · International Conference on Learning Representations · doi:10.48550/arxiv.2104.02297 · Cited by 62 · see citing works

XEdgeAI: A human-centered industrial inspection framework with data-centric Explainable Edge AI approach

Nguyen, T. T. H., Nguyen, L. P. T., & Cao, H. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102782 · Cited by 28 · see citing works

Physics-encoded deep learning for integrated modeling of watershed hydrology and reservoir operations

Yu, B., Zheng, Y., He, S., Xiong, R., & Wang, C. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133052 · Cited by 24 · see citing works

Neural network interpretability for forecasting of aggregated renewable generation

Lu, Y., Murzakhanov, I., & Chatzivasileiadis, S. (2021) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm51999.2021.9631993 · Cited by 23 · see citing works

Measured and projected beam backgrounds in the Belle II experiment at the SuperKEKB collider

Natochii, A., Browder, T. E., Cao, L., Cautero, G., Dreyer, S., Frey, A., Gabrielli, A., Giuressi, D., Ishibashi, T., Jin, Y., et al. (2023) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2023.168550 · Cited by 23 · see citing works

Evaluating feature attribution methods in the image domain

Gevaert, A., Rousseau, A.-J., Becker, T., Valkenborg, D., Bie, T. D., & Saeys, Y. (2024) · Machine Learning · doi:10.1007/s10994-024-06550-x · Cited by 23 · see citing works

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review

Mao, L., Wang, H., Hu, L., Tran, N. L., Canoll, P., Swanson, K. R., & Li, J. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3515839 · Cited by 24 · see citing works

Learning to Explain: Generating Stable Explanations Fast

Situ, X., Ingrid Zukerman, A., Paris, C. L., Maruf, S., & Haffari, G. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.415 · Cited by 22 · see citing works

PAUSE: principled feature attribution for unsupervised gene expression analysis

Janizek, J. D., Spiro, A. E., Çelik, S., Blue, B. W., RUSSELL, J. C., Lee, T.-I., Kaeberlin, M., & Lee, S. (2023) · Genome biology · doi:10.1186/s13059-023-02901-4 · Cited by 21 · see citing works

Harnessing Prior Knowledge for Explainable Machine Learning: An Overview

Beckh, K., Müller, S., Jakobs, M., Toborek, V., Tan, H., Fischer, R., Welke, P., Houben, S., & von Rueden, L. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00038 · Cited by 23 · see citing works

FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods

Hesse, R., Schaub-Meyer, S., & Roth, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00368 · Cited by 22 · see citing works

Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition

Hartog, P. B. R., Krüger, F., Genheden, S., & Tetko, I. V. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00824-1 · Cited by 22 · see citing works

A Weakly Supervised Gradient Attribution Constraint for Interpretable Classification and Anomaly Detection

Wargnier-Dauchelle, V., Grenier, T., Durand‐Dubief, F., Cotton, F., & Sdika, M. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3282789 · Cited by 21 · see citing works

Classically studied coherent structures only paint a partial picture of wall-bounded turbulence

Cremades, A., Hoyas, S., & Vinuesa, R. (2025) · Nature Communications · doi:10.1038/s41467-025-65199-9 · Cited by 20 · see citing works

CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species

Kempynck, N., Winter, S. D., Blaauw, C. H., Konstantakos, V., Ekşi, E. C., Dieltiens, S., Abaffyová, D., Bercier, V., Taskiran, I. I., Hulselmans, G. J., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03057-2 · Cited by 24 · see citing works

Artificial intelligence for nailfold capillaroscopy analyses – a proof of concept application in juvenile dermatomyositis

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Attributions for ML-based ICS Anomaly Detection: From Theory to Practice

Fung, C., Zeng, E., & Bauer, L. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23216 · Cited by 19 · see citing works

Structural impact on flame dynamics and mode transition in dual-swirl hydrogen flames

Jiang, J., Zhang, X., Yu, T., Sun, B., Zhou, H., & Ren, Z. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.110206 · Cited by 17 · see citing works

Explainable transfer learning for subsurface soil moisture prediction

Ye, S., Chai, Y., Li, J., Wang, J., Deng, X.-B., & Ran, Q. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133473 · Cited by 17 · see citing works

Human Rationales as Attribution Priors for Explainable Stance Detection

Jayaram, S., & Allaway, E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.450 · Cited by 17 · see citing works

Modeling Upscaled Mass Discharge From Complex DNAPL Source Zones Using a Bayesian Neural Network: Prediction Accuracy, Uncertainty Quantification and Source Zone Feature Importance

Kang, X., Kokkinaki, A., Shi, X., Lee, J. H., Guo, Z., Ni, L., & Wu, J. (2024) · Water Resources Research · doi:10.1029/2023wr036864 · Cited by 16 · see citing works

Review article: Harnessing data-driven methods for climate multi-hazard and multi-risk assessment

Ferrario, D. M., Sanò, M., Maraschini, M., Critto, A., & Torresan, S. (2026) · Natural hazards and earth system sciences · doi:10.5194/nhess-26-2975-2026 · Cited by 19 · see citing works

Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning

Decker, T. W., Lebacher, M., & Tresp, V. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096396 · Cited by 15 · see citing works

Explainable cross-task adaptive transfer learning for motor imagery EEG classification

Miao, M., Yang, Z., Zeng, H., Zhang, W., Bao-guo, X., & Hu, W. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad0c61 · Cited by 15 · see citing works

Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory GenomicsPreprint

Prakash, E., Shrikumar, A., & Kundaje, A. (2021) · bioRxiv · doi:10.1101/2021.12.26.474224 · Cited by 19 · see citing works

Generating and leveraging explanations of AI/ML models in materials and manufacturing research

Braham, E. J., Ruddock, J. M., & Hardin, J. O. (2025) · Patterns · doi:10.1016/j.patter.2025.101340 · Cited by 14 · see citing works

Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability

Gallo, M., Gallo, M., Nenutil, R., Němeček, M., Holub, P., & Brázdil, T. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.09.008 · Cited by 15 · see citing works

An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns

He, Q., Zhu, Z., Zhao, D., Song, W., & Huang, D. (2024) · Applied Sciences · doi:10.3390/app14020601 · Cited by 14 · see citing works

Best of both worlds: local and global explanations with human-understandable conceptsPreprint

Schrouff, J., Baur, S., Hou, S., Mincu, D., Loreaux, E., Blanes, R., Wexler, J., Karthikesalingam, A., & Kim, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08641 · Cited by 24 · see citing works

Combining data augmentation and hybrid modeling approaches for deep learning-based monthly streamflow forecasting

Xu, W., Chen, J., & Corzo, G. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133318 · Cited by 13 · see citing works

Studying How to Efficiently and Effectively Guide Models with Explanations

Rao, S., Böhle, M., Parchami-Araghi, A., & Schiele, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00184 · Cited by 13 · see citing works

Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost

Gorski, G. A., Cook, S. E., Snyder, A., Appling, A., Thompson, T., Smith, J. D., Warner, J. C., & Topp, S. (2024) · Limnology and Oceanography · doi:10.1002/lno.12549 · Cited by 12 · see citing works

Stream Nitrate Dynamics Driven Primarily by Discharge and Watershed Physical and Soil Characteristics at Intensively Monitored Sites: Insights From Deep Learning

Gorski, G. A., Larsen, L. G., Wingenroth, J., Zhang, L., Bellugi, D., & Appling, A. (2024) · Water Resources Research · doi:10.1029/2023wr036591 · Cited by 12 · see citing works

A Lightweight Low-Power Model for the Detection of Plant Leaf Diseases

Akuthota, U. C., Abhishek, A., & Bhargava, L. (2024) · SN Computer Science · doi:10.1007/s42979-024-02658-y · Cited by 11 · see citing works

Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma

Wang, H., Argenziano, M., Yoon, H., Boyett, D. M., Save, A., Petridis, P. D., Savage, W., Jackson, P., Hawkins-Daarud, A. J., Tran, N. L., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01277-4 · Cited by 11 · see citing works

Explainable time-series forecasting with sampling-free SHAP for Transformers

Hertel, M., Pütz, S., Mikut, R., Hagenmeyer, V., & Schäfer, B. (2026) · Nature Communications · doi:10.1038/s41467-026-73243-5 · Cited by 10 · see citing works

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Zwart, J. A., Diaz, J., Hamshaw, S. D., Oliver, S. K., Ross, J. C., Sleckman, M. J., Appling, A., Corson-Dosch, H. R., Jia, X., Read, J. S., et al. (2023) · Frontiers in Water · doi:10.3389/frwa.2023.1184992 · Cited by 10 · see citing works

SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence

Gunraj, H., Guerrier, P. K., Fernandez, S., & Wong, A. M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26858 · Cited by 10 · see citing works

Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients

Yang, M., Liu, S., Hao, T., Ma, C., Chen, H., Li, Y., Wu, C., Xie, J., Qiu, H., Li, J., et al. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102785 · Cited by 10 · see citing works

Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis

Lim, B. Y., Cahaly, J. P., Sng, C. Y. F., & Chew, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714058 · Cited by 12 · see citing works

Inferring wall-bounded coherent structures from two-dimensional turbulent fields via SHAP analysis

Molina-Casino, S., Cremades, A., Hoyas, S., Cardesa, J. I., Chedevergne, F., & Vinuesa, R. (2025) · European Journal of Mechanics - B/Fluids · doi:10.1016/j.euromechflu.2025.204304 · Cited by 10 · see citing works

Explainable image segmentation for spatio-temporal and multivariate image data in precipitation nowcasting

Ahangama, I., Meedeniya, D., & Pradhan, B. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.105595 · Cited by 10 · see citing works

Toward Automated Game Balance: A Systematic Engineering Design Approach

DeLaurentis, D. A., Panchal, J. H., Raz, A. K., Balasubramani, P., Maheshwari, A., Dachowicz, A. P., & Mall, K. (2021) · IEEE Conference on Games (CoG) · doi:10.1109/cog52621.2021.9619032 · Cited by 10 · see citing works

Local Path Integration for Attribution

Yang, P., Akhtar, N., Wen, Z., & Mian, A. S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25422 · Cited by 9 · see citing works

Length of stay prediction for hospital management using domain adaptation

Momo, L. N. W., Moorosi, N., Nsoesie, E. O., Rademakers, F. E., & Moor, B. D. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108088 · Cited by 9 · see citing works

Deep learning assisted monitoring framework for geological carbon sequestration

Zingaro, G., Ardakani, S. H., Gracie, R., & Leonenko, Y. (2025) · International journal of greenhouse gas control · doi:10.1016/j.ijggc.2025.104372 · Cited by 9 · see citing works

Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools

Feldhus, N., Schwarzenberg, R., & Möller, S. (2021) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2021.emnlp-demo.11 · Cited by 9 · see citing works

MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks

Zhu, Z., Chen, H., Zhang, J., Wang, X., Jin, Z., Xue, M., Zhu, D., & Choo, K. R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29669 · Cited by 9 · see citing works

Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN

Qiang, Y., Li, C., Brocanelli, M., & Zhu, D. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/103 · Cited by 9 · see citing works

Rationalizing predictions by adversarial information calibration

Sha, L., Camburu, O.-M., & Lukasiewicz, T. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103828 · Cited by 10 · see citing works

Privileged Attribution Constrained Deep Networks for Facial Expression Recognition

Bonnard, J., Dapogny, A., Dhombres, F., & Bailly, K. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956496 · Cited by 9 · see citing works

Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features

Witherow, M. A., Samad, M. D., Diawara, N., Bar, H., & Iftekharuddin, K. M. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3297075 · Cited by 9 · see citing works

Role of locality, fidelity and symmetry regularization in learning explainable representations

Ronco, M., & Camps‐Valls, G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126884 · Cited by 10 · see citing works

Explainable monotonic networks and constrained learning for interpretable classification and weakly supervised anomaly detection

Wargnier-Dauchelle, V., Grenier, T., Durand‐Dubief, F., Cotton, F., & Sdika, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111186 · Cited by 8 · see citing works

Error-controlled non-additive interaction discovery in machine learning models

Chen, W., Jiang, Y., Noble, W. S., & Lu, Y. Y. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01086-8 · Cited by 8 · see citing works

Learning collision risk proactively from naturalistic driving data at scale

Jiao, Y., Calvert, S. C., van Cranenburgh, S., & van Lint, H. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01189-w · Cited by 8 · see citing works

Weakly-Supervised Transfer Learning With Application in Precision Medicine

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ESSA: Explanation Iterative Supervision via Saliency-guided Data Augmentation

Gu, S., Zhang, Y., Gao, Y., Yang, X., & Zhao, L. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599336 · Cited by 8 · see citing works

Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy

Chen, X., Sun, Y., Hruška, E., Dixit, V., Yang, J., He, Y., Wang, Y., & Liu, F. (2025) · Newton · doi:10.1016/j.newton.2025.100066 · Cited by 7 · see citing works

Improved prediction of chlorophyll-a concentrations using advancing graph neural network variants

Yoon, S.-H., & Ahn, K.-H. (2025) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2025.179481 · Cited by 7 · see citing works

Learning Explainable Models Using Attribution Priors

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Learning Deep Attribution Priors Based On Prior KnowledgePreprint

Weinberger, E., Janizek, J. D., & Lee, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10065 · Cited by 13 · see citing works

Fast Axiomatic Attribution for Neural NetworksPreprint

Hesse, R., Schaub-Meyer, S., & Roth, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.07668 · Cited by 15 · see citing works

Axiomatic attribution for deep networks

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Visualizing and Generalizing Integrated Attributions

Payne, E., Patrick, D., & Fernandez, A. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78189-6_29