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Cited by 5Explainable Artificial Intelligence (XAI)Advanced Neural Network ApplicationsSoftware Testing and Debugging Techniques

The DOI matches its Crossref record; 18 of the 24 references it cites were found in a registry or an index.

Discriminative Attribution from Paired Images

Eckstein, Nils, Bukhari, Habib, Bates, Alexander S., Jefferis, Gregory S. X. E., Funke, Jan·2023·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-25069-9_27
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Registry

Crossref · 10.1007/978-3-031-25069-9_27

Cites

18 of 18 references verified0 citation errors · 0 not checkable

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Citations

Cites

Flagged first, then by citations

6 of the 24 references couldn’t be matched to a known paper.

Research works printed but not linked: 21

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  10. Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874 (2017) · arXiv
  11. Ribeiro, M.T., Singh, S., Guestrin, C.: Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386 (2016) · arXiv
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  15. Smilkov, D., Thorat, N., Kim, B., Viégas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017) · arXiv
  16. Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014) · arXiv
  17. Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning, Vol. 70, pp. 3319–3328. JMLR.org (2017)
  18. Verma, S., Dickerson, J., Hines, K.: Counterfactual explanations for machine learning: a review. arXiv preprint arXiv:2010.10596 (2020) · arXiv
  19. Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)
  20. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016) · arXiv
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Related

Metadata

Authors
Eckstein, N., Bukhari, H., Bates, A. S., Jefferis, G. S. X. E., & Funke, J.
Affiliations
  • Howard Hughes Medical Institute (US)
  • Harvard University (US)
  • MRC Laboratory of Molecular Biology (GB)
Corresponding author
Eckstein, Nils
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2023
Pages
406-422
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-25069-9_27
OpenAlex
W4320481824
Cited by
5
References
24
Citations by year
2026: 2 · 2024: 2
FWCI
0.71
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Lecture notes in computer science (publishedVersion)
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 › Software › Software Testing and Debugging Techniques
Keywords
feature attribution, counterfactual explanations, deep neural network interpretability, discriminative features, observer bias, synaptic morphology, Drosophila melanogaster, medical image analysis

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