Evaluating the Visualization of What a Deep Neural Network Has Learned

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839

Explaining and Harnessing Adversarial Examples

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381

An introduction to variable and feature selection

GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354

Domain-Adversarial Training of Neural Networks

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143

Discovering Molecular Functional Groups Using Graph Convolutional Neural NetworksPreprint

Pope, P., Kolouri, S., Rostrami, M., Martin, C. E., & Hoffmann, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00265 · Cited by 23

Revisiting Sanity Checks for Saliency MapsPreprint

Yona, G., & Greenfeld, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14297 · Cited by 14

CoughLIME: Sonified Explanations for the Predictions of COVID-19 Cough Classifiers

Wullenweber, A., Akman, A., & Schuller, B. W. (2022) · 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871291 · Cited by 13

Interpretability of Machine Learning Methods Applied to Neuroimaging

Thibeau–Sutre, E., Collin, S., Burgos, N., & Colliot, O. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_22 · Cited by 13

Importance-Aware Information Bottleneck Learning Paradigm for Lip Reading

Sheng, C., Liu, L., Deng, W., Bai, L., Liu, Z., Lao, S., Kuang, G., & Pietikäinen, M. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3210761 · Cited by 13

Towards Explainable Occupational Fraud Detection

Tritscher, J., Schlör, D., Gwinner, F., Krause, A., & Andreas Hotho, A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23633-4_7 · Cited by 13

Global geometry of chemical graph neural network representations in terms of chemical moieties

El-Samman, A. M., Incé A. Husain, Huynh, M., Castro, S. D., Morton, B., Baerdemacker, S. D., & Baerdemacker, S. D. (2024) · Digital Discovery · doi:10.1039/d3dd00200d · Cited by 14

On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness

Lamprou, V., Kallipolitis, A., & Maglogiannis, I. G. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108238 · Cited by 15

A Fresh Look at Sanity Checks for Saliency Maps

Hedström, A., Weber, L., Lapuschkin, S., & Höhne, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63787-2_21 · Cited by 13

Structuring Neural Networks for More Explainable Predictions

Rieger, L., Chormai, P., Montavon, G., Hansen, L. K., & Müller, K. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-98131-4_5 · Cited by 13

Enhanced Rotational Invariant Convolutional Neural Network for Supernovae Detection

Reyes, E., Estévez, P. A., Reyes, I., Cabrera-Vives, G., Huijse, P., Carrasco-Davis, R., & Förster, F. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489627 · Cited by 13

Enhancing Representation of Deep Features for Sensor-Based Activity Recognition

Li, X., Nie, L., Si, X., Ding, R., & Zhan, D. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01689-y · Cited by 13

New Insights for the Design of Bionic Robots: Adaptive Motion Adjustment Strategies During Feline Landings

Xu, D., Zhou, H., Jiang, X., Li, S., Zhang, Q., Baker, J. S., & Gu, Y. (2022) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.836043 · Cited by 13

ExSum: From Local Explanations to Model Understanding

Zhou, Y., Ribeiro, M. T., & Shah, J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.392 · Cited by 18

Logic Traps in Evaluating Attribution Scores

Ju, Y., Zhang, Y., Zhao, Y., Jiang, Z., Liu, K., & Zhao, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.407 · Cited by 23

Iterative deblending using unsupervised learning with double-deep neural networks

Wang, K., Hu, T., & Wang, S. (2023) · Geophysics · doi:10.1190/geo2022-0299.1 · Cited by 13

Explaining What a Neural Network has Learned: Toward Transparent Classification

Amarasinghe, K., & Manic, M. (2019) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2019.8858899 · Cited by 15

Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning

Lee, K., Vlahov, B., Gibson, J., Rehg, J. M., & Theodorou, E. A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560916 · Cited by 13

Fast multi-resolution occlusion: a method for explaining and understanding deep neural networks

Behzadi-Khormouji, H., & Rostami, H. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01946-3 · Cited by 13

Measuring and Improving Faithfulness of Attention in Neural Machine Translation

Moradi, P., Kambhatla, N., & Sarkar, A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.243 · Cited by 14

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

Chen, H., Feng, S., Ganhotra, J., Wan, H., Gunasekara, C., Joshi, S., & Ji, Y. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.306 · Cited by 15

Towards Ground Truth Evaluation of Visual ExplanationsPreprint

Osman, A., Arras, L., & Samek, W. (2020) · arXiv (Cornell University) · Cited by 13

Using Neural Networks to Predict Micro-Spatial Economic Growth

Khachiyan, A., Thomas, A. H., Zhou, H., Hanson, G., Cloninger, A., Rosing, T., & Khandelwal, A. K. (2021) · National Bureau of Economic Research · doi:10.3386/w29569 · Cited by 13

Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?Preprint

Jin, W., Li, X., & Hamarneh, G. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.06487 · Cited by 13

Combining theoretical modelling and machine learning approaches: The case of teamwork effects on individual effort expenditure

Eisbach, S., Mai, O., & Hertel, G. (2024) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2024.101077 · Cited by 12

Towards Explainable Deep Learning in Computational Neuroscience: Visual and Clinical Applications

Mehmood, A., Mehmood, F., & Kim, J. (2025) · Mathematics · doi:10.3390/math13203286 · Cited by 14

Caffe2Unity: Immersive Visualization and Interpretation of Deep Neural Networks

Aamir, A., Tamošiūnaitė, M., & Wörgötter, F. (2021) · Electronics · doi:10.3390/electronics11010083 · Cited by 12

Explaining COVID-19 diagnosis with Taylor decompositions

Hassan, M. M., AlQahtani, S. A., Alelaiwi, A. A., & Papa, J. P. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08021-7 · Cited by 13

Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics

Ellis, C. A., Miller, R. L., & Calhoun, V. D. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100186 · Cited by 12

Towards symbolic XAI — explanation through human understandable logical relationships between features

Schnake, T., Jafari, F. R., Lederer, J., Xiong, P., Nakajima, S., Gugler, S., Montavon, G., & Müller, K. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102923 · Cited by 18

Deep Neural Model Inspection and Comparison via Functional Neuron Pathways

Fiacco, J., Choudhary, S., & Rosé, C. P. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1575 · Cited by 12

Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

Rieckmann, A., Dworzyński, P., Arras, L., Lapuschkin, S., Samek, W., Arah, O. A., Rod, N. H., & Ekstrøm, C. T. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac078 · Cited by 12

Benchmarking explanation methods for mental state decoding with deep learning models

Thomas, A. W., Ré, C., & Poldrack, R. A. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120109 · Cited by 17

Strategies to Exploit XAI to Improve Classification Systems

Apicella, A., Lorenzo, L. D., Isgrò, F., Pollastro, A., & Prevete, R. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44064-9_9 · Cited by 15

Nonlinear VW-SAE Based Deep Learning for Quality-Related Feature Learning and Soft Sensor Modeling

Yuan, X., Ou, C., Wang, Y., & Yang, C. (2018) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2018.8591240 · Cited by 12

Hybrid fuzzy integrated convolutional neural network (HFICNN) for similarity feature recognition problem in abnormal netflow detection

Yue, X., Wang, J., & Huang, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.076 · Cited by 12

Machine and Deep Learning

Chang, A. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00005-6 · Cited by 12

Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations

Losch, M., Fritz, M., & Schiele, B. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01498-0 · Cited by 13

Flexible Instance-Specific Rationalization of NLP Models

Chrysostomou, G., & Αλέτρας, Ν. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21298 · Cited by 14

Clothing and People - A Social Signal Processing Perspective

Aghaei, M., Parezzan, F., Dimiccoli, M., Radeva, P. I., & Cristani, M. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.71 · Cited by 12

Gradient-based feature-attribution explainability methods for spiking neural networks

Bitar, A., Rosales, R., & Paulitsch, M. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1153999 · Cited by 17

Toward Learning Model-Agnostic Explanations for Deep Learning-Based Signal Modulation Classifiers

Tian, Y., Xu, D., Tong, E., Sun, R., Chen, K., Li, Y., Baker, T., Niu, W., & Liu, J. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3367780 · Cited by 11

Explaining predictive uncertainty by exposing second-order effects

Bley, F., Lapuschkin, S., Samek, W., & Montavon, G. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111171 · Cited by 11

Evaluating the effectiveness of local explanation methods on source code-based defect prediction models

Gao, Y., Zhu, Y., & Yu, Q. (2022) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3524842.3528472 · Cited by 11

A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays

Vardhan, A., Makhnevich, A., Omprakash, P., Hirschorn, D. S., Barish, M. A., Cohen, S. L., & Zanos, T. P. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-022-00103-0 · Cited by 12

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females

Georgievskaya, A., Tlyachev, T., Kiselev, K., Hillebrand, G. G., Chekanov, K., Danko, D., Golodyayev, A., & Majmudar, G. (2024) · Experimental Dermatology · doi:10.1111/exd.15045 · Cited by 12

Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison

Brunke, L., Agrawal, P., & George, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66415-2_8 · Cited by 10

Improving Feature Attribution through Input-specific Network PruningPreprint

Khakzar, A., Baselizadeh, S., Khanduja, S., Rupprecht, C., Kim, S. T., & Navab, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11081 · Cited by 11

An Explainable Deep Learning Approach for Multimodal Electrophysiology ClassificationPreprint

Ellis, C. A., Carbajal, D. A., Zhang, R., Miller, R. L., Calhoun, V. D., & Wang, M. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.12.443594 · Cited by 11

Explainability is not Enough : Requirements for Human-AI-Partnership in Complex Socio-Technical Systems

Waefler, T., & Schmid, U. (2021) · doi:10.20378/irb-49775 · Cited by 11

Visualization Method Corresponding to Regression Problems and Its Application to Deep Learning-Based Gaze Estimation Model

Kanda, D., Kawai, S., & Nobuhara, H. (2020) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2020.p0676 · Cited by 10

Interpretability of deep neural networks used for the diagnosis of Alzheimer's disease

Pohl, T., Jakab, M., & Benešová, W. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22657 · Cited by 11

A new method applied for explaining the landing patterns: Interpretability analysis of machine learning

Xu, D., Zhou, H., Quan, W., Ugbolue, U. C., Fekete, G., & Gu, Y. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26052 · Cited by 11

Coarse-to-Fine: A hierarchical DNN inference framework for edge computing

Zhang, Z., Zhang, Y., Bao, W., Li, C., & Yuan, D. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.009 · Cited by 10

Artificial Intelligence in Neuro Degenerative Diseases: Opportunities and Challenges

Rajaraman, S. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-53148-4_8 · Cited by 12

Forensic research of satellite images forgery: a comprehensive survey

Ding, X., Nie, Y., Yao, J., Tang, J., & Lang, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10909-w · Cited by 10

Hybrid machine learning framework for predicting pozzolanic reactivity: Integration of R3 test and GAN-augmented data

Jeon, D., Han, S., Jung, S. K., & Moon, J. (2025) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2025.141959 · Cited by 11

Explaining 3D Semantic Segmentation Through Generative AI ‐Based Counterfactuals

Rozajac, D., Lukač, N., Schweng, S., Gollob, C., Nothdurft, A., Stampfer, K., Ser, J. D., & Holzinger, A. (2025) · Expert Systems · doi:10.1111/exsy.70163 · Cited by 11

Spatio-Temporal Perturbations for Video Attribution

Li, Z., Wang, W., Li, Z., Huang, Y., & Sato, Y. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3081761 · Cited by 11

Explainable gait recognition with prototyping encoder–decoder

Moon, J., Shin, Y.-M., Park, J.-D., Minaya, N. H., Shin, W.-Y., & Choi, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264783 · Cited by 11

Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts

Gandomkar, Z., Khong, P. L., Punch, A., & Lewis, S. J. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00631-w · Cited by 13

LRP2A: Layer-wise Relevance Propagation based Adversarial attacking for Graph Neural Networks

Liu, L., Du, Y. P., Wang, Y., Cheung, W. K., Zhang, Y., Liu, Q., & Wang, G. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109830 · Cited by 10

Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images

Sadafi, A., Adonkina, O., Khakzar, A., Lienemann, P., Hehr, M., Rueckert, D., Navab, N., & Marr, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_14 · Cited by 10

HEAR4Health: a blueprint for making computer audition a staple of modern healthcare

Triantafyllopoulos, A., Kathan, A., Baird, A., Christ, L., Gebhard, A., Gerczuk, M., Karas, V., Hübner, T., Jing, X., Liu, S., et al. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1196079 · Cited by 12

A Multi-Phased Co-design of an Interactive Analytics System for MOBA Game Occurrences

Li, Q., Wu, Z., Xu, P. F., Qu, H., & Ma, X. (2018) · Designing Interactive Systems Conference · doi:10.1145/3196709.3196771 · Cited by 10

A Life Cycle for Modeling Biology at Different Scales

Harline, K., Martínez‐Gómez, J., Specht, C. D., & Roeder, A. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.710590 · Cited by 10

Toward Understanding Acceleration-based Activity Recognition Neural Networks with Activation Maximization

Yoshimura, N., Maekawa, T., & Hara, T. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533888 · Cited by 10

Unified ICH quantification and prognosis prediction in NCCT images using a multi-task interpretable network

Gong, K., Dai, Q., Wang, J., Zheng, Y., Shi, T., Yu, J., Chen, J., Huang, S., & Wang, Z. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1118340 · Cited by 10

Ensembling Visual Explanations

Rajani, N. F., & Mooney, R. J. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-98131-4_7 · Cited by 10

Software and Application Patterns for Explanation Methods

Alber, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_22 · Cited by 12

Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features

Khakzar, A., Zhang, Y., Mansour, W., Cai, Y., Li, Y., Zhang, Y., Kim, S. T., & Navab, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_37 · Cited by 10

Practical Machine Learning Safety: A Survey and PrimerPreprint

Mohseni, S., Wang, H., Yu, Z., Xiao, C., Wang, Z., & Yadawa, J. (2021) · arXiv (Cornell University) · Cited by 10

Robust Models Are More Interpretable Because Attributions Look NormalPreprint

Wang, Z., Fredrikson, M., & Datta, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11257 · Cited by 14

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram DataPreprint

Ellis, C. A., Sattiraju, A., Miller, R. L., & Calhoun, V. D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.20.533467 · Cited by 10

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

Deep Attentive Model for Knowledge Tracing

Wang, X., Chen, L., & Zhang, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26214 · Cited by 11

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

Explainable AI-Based Interface System for Weather Forecasting Model

Kim, S., Choi, Y., Choi, Y., Lee, S., Stitsyuk, A., Park, M.-K., Jeong, S., Baek, Y.-S., & Choi, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48057-7_7 · Cited by 9

Deep reinforcement learning imbalanced credit risk of SMEs in supply chain finance

Zhang, W., Yan, S., Li, J., Peng, R., & Tian, X. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-05921-w · Cited by 9

Explainable artificial intelligence in deep learning–based detection of aortic elongation on chest X-ray images

Ribeiro, E., Cárdenas, D. A. C., Dias, F. M., Krieger, J. E., & Gutierrez, M. A. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae045 · Cited by 9

EvaluateXAI: A framework to evaluate the reliability and consistency of rule-based XAI techniques for software analytics tasks

Awal, M. A., & Roy, C. K. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112159 · Cited by 9

Propagating Transparency: A Deep Dive into the Interpretability of Neural Networks

Somani, A., Horsch, A., Bopardikar, A. S., & Prasad, D. K. (2024) · Nordic Machine Intelligence · doi:10.5617/nmi.10755 · Cited by 9

Visual interpretation of deep learning model in ECG classification: A comprehensive evaluation of feature attribution methods

Suh, J., Kim, J., Kwon, S., Jung, E., Ahn, H., Lee, K.-Y., Choi, E., & Rhee, W. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109088 · Cited by 9

A residual deep learning framework for sleep apnea diagnosis from single lead electrocardiogram signals: An explainable artificial intelligence approach

Ganguly, B., Dasgupta, R., & Dey, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110481 · Cited by 9

Learning Propagation Rules for Attribution Map Generation

Yang, Y., Qiu, J., Song, M., Tao, D., & Wang, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_40 · Cited by 9

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

Iravani, S., & Conrad, T. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3141656 · Cited by 10

Optimisation of Deep Learning Small-Object Detectors with Novel Explainable Verification

Mohamed, E., Sirlantzis, K., Howells, G., & Hoque, S. (2022) · Sensors · doi:10.3390/s22155596 · Cited by 9

Using Neural Networks to Predict Micro-Spatial Economic Growth

Khachiyan, A., Thomas, A. H., Zhou, H., Hanson, G., Cloninger, A., Rosing, T., & Khandelwal, A. K. (2021) · American Economic Review Insights · doi:10.1257/aeri.20210422 · Cited by 23

Local Interpretation of Transformer Based on Linear Decomposition

Yang, S., Huang, S., Zou, W., Zhang, J., Dai, X., & Chen, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.572 · Cited by 11

Reliability of Deep Neural Networks for an End-to-End Imitation Learning-Based Lane Keeping

Liu, S., & Müller, S. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3299229 · Cited by 9

Explanation strategies in humans versus current explainable artificial intelligence: Insights from image classification

Qi, R., Zheng, Y., Yang, Y., Cao, C. C., & Hsiao, J. H. (2024) · British Journal of Psychology · doi:10.1111/bjop.12714 · Cited by 19

Explainable data-driven Q-learning control for a class of discrete-time linear autonomous systems

Perrusquía, A., Zou, M., & Guo, W. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121283 · Cited by 9

What Sketch Explainability Really Means for Downstream Tasks?

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