The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Prior knowledge can be utilized to constrain explanations during training to improve model performance and produce high-quality attribution maps [37].”
From Transparency of deep neural networks for medical image analysis: A review of interpretability methods (2022) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
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
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Self-citationTensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551
Self-citationInception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087
5th International Conference on Learning Representations (ICLR 17)
Havrylov, S., & Titov, I. (2017) · Cited by 3,503
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J., Adams, T., Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0304-0 · Cited by 1,967
Self-citationCapturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · cited 1×
“Current research also investigates the axioms that attribution methods should satisfy 1–4 and how they provide insight into model behaviour 5–8 .”
BackgroundTheoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., & Jordan, M. I. (2019) · arXiv · doi:10.48550/arxiv.1901.08573 · Cited by 1,560
Prognostically Useful Gene-Expression Profiles in Acute Myeloid Leukemia
Valk, P. J. M., Verhaak, R. G. W., Beijen, M. A., Erpelinck, C. A. J., van Doorn-Khosrovani, S. B. V. W., Boer, J. M., Beverloo, H. B., Moorhouse, M. J., van der Spek, P. J., Löwenberg, B., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040465 · Cited by 1,370
Functional genomic landscape of acute myeloid leukaemia
Tyner, J. W., Tognon, C. E., Bottomly, D., Wilmot, B., Kurtz, S. E., Savage, S. L., Long, N., Schultz, A. R., Traer, E. A., Abel, M. L., et al. (2018) · Nature · doi:10.1038/s41586-018-0623-z · Cited by 1,359
Very deep convolutional neural network based image classification using small training sample size
Liu, S., & Deng, W. (2015) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2015.7486599 · Cited by 1,181
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Fong, R. C., & Vedaldi, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.371 · Cited by 1,097
Adversarial Examples Are Not Bugs, They Are Features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., & Madry, A. (2019) · arXiv · doi:10.48550/arxiv.1905.02175 · Cited by 971
Understanding multicellular function and disease with human tissue-specific networks
Greene, C. S., Krishnan, A., Wong, A. K., Ricciotti, E., Zelaya, R. A., Himmelstein, D. S., Zhang, R., Hartmann, B. M., Zaslavsky, E., Sealfon, S. C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3259 · Cited by 832
Comparing Measures of Sparsity
Hurley, N., & Rickard, S. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2027527 · Cited by 772
Datta, A., Sen, S., & Zick, Y. (2016) · 2016 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2016.42 · Cited by 728
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Preuer, K., Lewis, R. P. I., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx806 · Cited by 668
Ross, A. S., & Doshi‐Velez, F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11504 · Cited by 647
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Ross, A. S., Hughes, M. C., & Doshi-Velez, F. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/371 · Cited by 515
Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K., & Samek, W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44781-0_8 · Cited by 512
Group sparse regularization for deep neural networks
Scardapane, S., Comminiello, D., Hussain, A., & Uncini, A. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.029 · Cited by 467
Sayres, R., Taly, A., Rahimy, E., Blumer, K., Coz, D., Hammel, N., Krause, J., Narayanaswamy, A., Rastegar, Z., Wu, D., et al. (2019) · Ophthalmology · doi:10.1016/j.ophtha.2018.11.016 · Cited by 418
Ross, A. S., & Doshi‐Velez, F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.09404 · Cited by 265
Explainable AI for Trees: From Local Explanations to Global UnderstandingPreprint
Lundberg, S., Erion, G., Chen, H., DeGrave, A. J., Prutkin, J. M., Nair, B. G., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04610 · Cited by 252
Self-citationImproving generalization performance using double backpropagation
Drucker, H., & LeCun, Y. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.165600 · Cited by 238
A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing
Lou, Y., Zeng, T., Osher, S., & Xin, J. X. (2015) · SIAM Journal on Imaging Sciences · doi:10.1137/14098435x · Cited by 235
Schramowski, P., Stammer, W., Teso, S., Brugger, A., Herbert, F., Shao, X., Luigs, H.-G., Mahlein, A.-K., & Kersting, K. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0212-3 · Cited by 207
Improving DNN Robustness to Adversarial Attacks Using Jacobian Regularization
Jakubovitz, D., & Giryes, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_32 · Cited by 205
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMsPreprint
Murdoch, W. J., Liu, P. J., & Yu, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.05453 · Cited by 191
Interpretations are useful: penalizing explanations to align neural networks with prior knowledgePreprint
Rieger, L., Singh, C., Murdoch, W. J., & Yu, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.13584 · Cited by 148
Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples
Zonoobi, D., Kassim, A. A., & Venkatesh, Y. V. (2011) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2011.2160711 · Cited by 133
Incorporating Priors with Feature Attribution on Text Classification
Liu, F., & Avci, B. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1631 · Cited by 120
Paths and consistency in additive cost sharing
Friedman, E. J. (2004) · International Journal of Game Theory · doi:10.1007/s001820400173 · Cited by 57
Robust Attribution RegularizationPreprint
Chen, J., Wu, X., Rastogi, V., Liang, Y., & Jha, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09957 · Cited by 44
Graph-regularized dual Lasso for robust eQTL mapping
Cheng, W. D., Zhang, X., Guo, Z., Shi, Y., & Wang, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu293 · Cited by 36
Liu, J., Qin, Y., Yang, S., Wang, Y., Chang, Y., Zhao, T., Jiang, Q., & Huang, X. (2017) · Annals of Hematology · doi:10.1007/s00277-016-2913-6 · Cited by 26
Laplace-distributed increments, the Laplace prior, and edge-preserving regularization
Bardsley, J. M. (2012) · Journal of Inverse and Ill-Posed Problems · doi:10.1515/jip-2012-0017 · Cited by 23
Adversarially Robust Training through Structured Gradient RegularizationPreprint
Roth, K., Lucchi, A., Nowozin, S., & Hofmann, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08736 · Cited by 16
Towards Robust Training of Neural Networks by Regularizing Adversarial GradientsPreprint
Yu, F., Xu, Z., Wang, Y., Liu, C., & Chen, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09370 · Cited by 8
Supervising Feature InfluencePreprint
Sen, S., Mardziel, P., Datta, A., & Fredrikson, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10815 · Cited by 6
suinleelab/attributionpriors: Nature Machine Intelligence code
Sturmfels, P., Erion, G., & Janizek, J. D. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4608599 · Cited by 1
Self-citationsuinleelab/attributionpriors: Nature Machine Intelligence code
Sturmfels, P., Erion, G., & Janizek, J. D. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4608598 · Cited by 1
Self-citation
Research works printed but not linked: 9
- Smilkov, D., Thorat, N., Kim, B., Viégas, F. & Wattenberg, M. Smoothgrad: removing noise by adding noise. Preprint at https://arxiv.org/abs/1706.03825 (2017). · arXiv
- Recht, B., Roelofs, R., Schmidt, L. & Shankar, V. Do ImageNet classifiers generalize to ImageNet? Proc. of the 36th International Conference on Machine Learning Vol. 97, 5389–5400 (PMLR, 2019).
- Tsipras, D., Santurkar, S., Engstrom, L., Turner, A. & Madry, A. Robustness may be at odds with accuracy. In 7th International Conference on Learning Representations (ICLR, 2019).
- Feng, J. & Simon, N. Sparse-input neural networks for high-dimensional nonparametric regression and classification. Preprint at https://arxiv.org/abs/1711.07592 (2017). · arXiv
- Ross, A., Lage, I. & Doshi-Velez, F. The neural lasso: local linear sparsity for interpretable explanations. In Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments, 31st Conference on Neural Information Processing Systems (2017).
- Zhang, H., Cisse, M., Dauphin, Y. N. & Lopez-Paz, D. mixup: beyond empirical risk minimization. In 6th International Conference on Learning Representations (ICLR, 2018).
- Shi, Y. & Chang, Q. Efficient algorithm for isotropic and anisotropic total variation deblurring and denoising. J. Appl. Math. 2013, 797239 (2013).
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).
- Kingma, D. P. & Ba, J. In 3rd International Conference on Learning Representations (eds. Bengio, Y. & LeCun, Y.) (ICLR, 2015).
Cited by
Sorted by citations
AI for radiographic COVID-19 detection selects shortcuts over signal
DeGrave, A. J., Janizek, J. D., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00338-7 · Cited by 631
Self-citationSalahuddin, Z., Woodruff, H. C., Chatterjee, A., & Lambin, P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105111 · Cited by 508
Deep learning for water quality
Zhi, W., Appling, A., Golden, H. E., Podgorski, J. E., & Li, L. (2024) · Nature Water · doi:10.1038/s44221-024-00202-z · Cited by 399
Tripathy, K. P., & Mishra, A. K. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.130458 · Cited by 362
Jiang, S., Zheng, Y., Wang, C., & Babovic, V. (2021) · Water Resources Research · doi:10.1029/2021wr030185 · Cited by 257
Metadata
- Authors
- Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S. M., & Lee, S.-I.
- Affiliations
- University of Washington (US)
- Microsoft (United States) (US)
- Corresponding author
- Lee, Su-In
- Venue
- Nature Machine Intelligence
- Published
- 2021-05-31
- Volume
- 3
- Issue
- 7
- Pages
- 620-631
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3105856249
- Cited by
- 300
- References
- 54
- Citations by year
- 2026: 85 · 2025: 61 · 2024: 65 · 2023: 52 · 2022: 17 · 2021: 20
- FWCI
- 13.73
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (R35GM128638, F30HL151074-01, 5T32GM007266-46, R01AG061132, DGE-1256082)
- National Science Foundation (DBI-1552309, DBI-1759487)
- American Cancer Society (RSG-14-257-01-TBG)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- feature attribution, model performance improvement, completeness axiom, neural network interpretability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
