The DOI matches its OpenAlex record; 1 of the 1 references it cites were found in a registry or an index.
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
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Citations
Cites
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
Cited by
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
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
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
“One line of work focuses on providing an overview of different interpretation methods with a strong emphasis on post hoc interpretations of deep learning models (8, 9), sometimes pointing out similarities between various methods (10, 11).”
BackgroundExplaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477
“Both the DeepExplain [4] and iNNvestigate [3] toolboxes built on top of the popular Keras [30] package for Python with TensorFlow backend for explaining Deep Neural Network models, and thus provide support for both CPU and GPU hardware and convenient access for users of Keras models.”
MethodInfluentialTechniques for interpretable machine learning
Du, M., Liu, N., & Hu, X. (2019) · Communications of the ACM · doi:10.1145/3359786 · Cited by 1,268
Metadata
- Authors
- Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H.
- Venue
- Zurich Open Repository and Archive (University of Zurich)
- Publisher
- University of Zurich
- Published
- 2017-11-16
- Pages
- 0-0
- OpenAlex
- W2785760873
- Cited by
- 368
- References
- 1
- Type
- posted content
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
