Feature visualization

Advances in neural information processing systems 7

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Intriguing properties of neural networks

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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258 · see citing works

A Neural Algorithm of Artistic Style

Gatys, L., Ecker, A., & Bethge, M. (2016) · Journal of Vision · doi:10.1167/16.12.326 · Cited by 2,612 · see citing works

Understanding deep image representations by inverting them

Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827 · see citing works

Deconvolution and Checkerboard Artifacts

Odena, A., Dumoulin, V., & Olah, C. (2016) · Distill · doi:10.23915/distill.00003 · Cited by 1,639 · see citing works

Network Dissection: Quantifying Interpretability of Deep Visual Representations

Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.354 · Cited by 1,389 · see citing works

Inceptionism: Going Deeper into Neural Networks

Mordvintsev, A., Olah, C., & Tyka, M. (2015) · Cited by 569 · see citing works

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent SpacePreprint

Nguyen, A. Q., Clune, J., Bengio, Y., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00005 · Cited by 159 · see citing works

Understanding Intra-Class Knowledge Inside CNNPreprint

Wei, D., Zhou, B., Torrabla, A., & Freeman, W. T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.02379 · Cited by 79 · see citing works

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works

Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Hendrycks, D., & Dietterich, T. (2019) · arXiv · doi:10.48550/arxiv.1903.12261 · Cited by 1,335 · see citing works

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328 · see citing works

Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges

Molnar, C., Casalicchio, G., & Bischl, B. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-65965-3_28 · Cited by 677 · see citing works

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

Hohman, F., Kahng, M., Pienta, R., & Chau, D. H. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2843369 · Cited by 582 · see citing works

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M., & Seifert, C. (2023) · ACM Computing Surveys · doi:10.1145/3583558 · Cited by 590 · see citing works

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., et al. (2019) · JAMA Cardiology · doi:10.1001/jamacardio.2019.0640 · Cited by 318 · see citing works

Zoom In: An Introduction to Circuits

Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., & Carter, S. (2020) · Distill · doi:10.23915/distill.00024.001 · Cited by 288 · see citing works

Deep reinforcement learning and its neuroscientific implications

Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269 · see citing works

Neural Prototype Trees for Interpretable Fine-grained Image Recognition

Nauta, M., van Bree, R., & Seifert, C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01469 · Cited by 269 · see citing works

Single cortical neurons as deep artificial neural networks

Beniaguev, D., Segev, I., & London, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.002 · Cited by 254 · see citing works

The neuroconnectionist research programme

Doerig, A., Sommers, R. P., Seeliger, K., Richards, B., Ismael, J., Lindsay, G. W., Kording, K. P., Konkle, T., van Gerven, M. A. J., Kriegeskorte, N., et al. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00705-w · Cited by 233 · see citing works

Causal interpretability for machine learning-problems, methods and evaluation

Moraffah, R., Karami, M., Guo, R., Raglin, A., & Liu, H. (2020) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3400051.3400058 · Cited by 238 · see citing works

Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task

Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200 · see citing works

Guidelines and evaluation of clinical explainable AI in medical image analysis

Jin, W., Li, X., Fatehi, M., & Hamarneh, G. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102684 · Cited by 199 · see citing works

From attribution maps to human-understandable explanations through Concept Relevance Propagation

Achtibat, R., Dreyer, M., Eisenbraun, I., Bosse, S., Wiegand, T., Samek, W., & Lapuschkin, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00711-8 · Cited by 181 · see citing works

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer-wise relevance propagation

Eitel, F., Soehler, E., Bellmann-Strobl, J., Brandt, A. U., Ruprecht, K., Giess, R. M., Kuchling, J., Asseyer, S., Weygandt, M., Haynes, J.-D., et al. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.102003 · Cited by 171 · see citing works

Interpretation and visualization techniques for deep learning models in medical imaging

Huff, D. T., Weisman, A. J., & Jeraj, R. (2021) · Physics in Medicine & Biology · doi:10.1088/1361-6560/abcd17 · Cited by 172 · see citing works

Looking beyond the hype: applied AI and machine learning in translational medicine

Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159 · see citing works

Splicing ViT Features for Semantic Appearance Transfer

Tumanyan, N., Bar-Tal, O., Bagon, S., & Dekel, T. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01048 · Cited by 153 · see citing works

Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs

Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445088 · Cited by 117 · see citing works

Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks

Räuker, T., Ho, A., Casper, S., & Hadfield-Menell, D. (2023) · 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00039 · Cited by 122 · see citing works

Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis

Natekar, P., Kori, A., & Krishnamurthi, G. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00006 · Cited by 103 · see citing works

Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization

Farahat, A., Reichert, C., Sweeney-Reed, C. M., & Hinrichs, H. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab3bb4 · Cited by 92 · see citing works

Predicting cellular responses to perturbation across diverse contexts with State

Adduri, A. K., Gautam, D., Bevilacqua, B., Naghipourfar, M., Imran, A., Shah, R., Teyssier, N., Verma, R., Carpenter, C., Eraslan, B., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.052 · Cited by 100 · see citing works

Towards relatable explainable AI with the perceptual process

Zhang, W., & Lim, B. Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501826 · Cited by 67 · see citing works

Model metamers reveal divergent invariances between biological and artificial neural networks

Feather, J., Leclerc, G., Mądry, A., & McDermott, J. H. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01442-0 · Cited by 59 · see citing works

Evaluating the Interpretability of Generative Models by Interactive Reconstruction

Ross, A., Chen, N., Hang, E. Z., Glassman, E. L., & Doshi-Velez, F. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445296 · Cited by 40 · see citing works

Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation

Toms, B. A., Kashinath, K., Prabhat, & Yang, D. (2021) · Geoscientific Model Development · doi:10.5194/gmd-14-4495-2021 · Cited by 20 · see citing works

Towards a science exocortex

Yager, K. G. (2024) · Digital Discovery · doi:10.1039/d4dd00178h · Cited by 12 · see citing works

Re-evaluating Theory of Mind evaluation in large language models

Hu, J., Sosa, F., & Ullman, T. (2025) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2023.0499 · Cited by 10 · see citing works

When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education

Wang, P., Viberg, O., Law, E. L.-C., & Greiff, S. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10202-y · Cited by 3 · see citing works

Mechanistic interpretability for steering vision-language-action models

Häon, B., Stocking, K., Chuang, I., & Tomlin, C. (2025) · arXiv · doi:10.48550/arxiv.2509.00328 · Cited by 1 · see citing works

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Discussion on the Application of Visualization Technology in Modeling and Simulation

Na, Y., Shu, Y., Chang, L., et al. (2011) · International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII) · doi:10.1109/iciii.2011.336 · Cited by 5 · see citing works

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

Study and Two Types of Typical Usage of DataGrid Web Server Control

Hai-tao, L. (2005) · Modern Computer

Achieving Parameter of DBSCAN Based on Datagrid

Xu, Q. (2010)