The DOI matches its OpenAlex record; 3 of the 3 references it cites were found in a registry or an index.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
Cited by
Items 501–510 of 510
Guo, H., Yang, J., Liu, S., Qin, T., Zhao, Q., Hou, X., & Ren, L. (2021) · Bioengineered · doi:10.1080/21655979.2021.1957645 · Cited by 7
A novel connectivity of deep convolutional neural networks
Shen, Z., & Liu, Y. (2017) · 2017 Chinese Automation Congress (CAC) · doi:10.1109/cac.2017.8244187 · Cited by 5
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
Guénais, T., Vamvourellis, D., Yacoby, Y., Doshi-Velez, F., & Pan, W. (2020) · arXiv · doi:10.48550/arxiv.2007.06096 · Cited by 5
Hybrid descriptor for placental maturity grading
Lei, B., Jiang, F., Zhou, F., Ni, D., Yao, Y., Chen, S., & Wang, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08489-x · Cited by 3
Schuster, T., Kalyan, A., Polozov, O., & Kalai, A. T. (2021) · arXiv · doi:10.48550/arxiv.2106.05784 · Cited by 2
Connecting Context-specific Adaptation in Humans to Meta-learning
Dubey, R., Grant, E., Luo, M., Narasimhan, K., & Griffiths, T. (2020) · arXiv · doi:10.48550/arxiv.2011.13782 · Cited by 1
Quantifying Generalization in Reinforcement Learning
Cobbe, K., Klimov, O., Hesse, C., Kim, T., & Schulman, J. (2018) · arXiv · doi:10.48550/arxiv.1812.02341
Improving robustness against common corruptions by covariate shift adaptation
Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., & Bethge, M. (2020) · arXiv · doi:10.48550/arxiv.2006.16971
Order Matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., & Kudlur, M. (2015) · arXiv · doi:10.48550/arxiv.1511.06391
Automatic tagging using deep convolutional neural networks
Choi, K., Fazekas, G., & Sandler, M. (2016) · arXiv · doi:10.48550/arxiv.1606.00298
Metadata
- Authors
- Ioffe, S.
- Venue
- arXiv (Cornell University)
- Publisher
- Cornell University
- Published
- 2024-01-01
- ISSN
- 2331-8422 (ISSN-L)
- OpenAlex
- W2949117887
- Cited by
- 14,214
- References
- 3
- Type
- posted content
- Language
- en
- Open access
- green
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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.
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Data sources
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.
