The DOI matches its Crossref record; 192 of the 192 references it cites were found in a registry or an index.
A systematic review on overfitting control in shallow and deep neural networks
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
Cited by
Survey of Deep Learning Accelerators for Edge and Emerging Computing
Alam, S., Yakopcic, C., Wu, Q., Barnell, M., Khan, S., & Taha, T. M. (2024) · Electronics · doi:10.3390/electronics13152988 · Cited by 38
Roberts, C., Avendaño-Valencia, L. D., & García Cava, D. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.111028 · Cited by 15
Metadata
- Authors
- Bejani, M. M., & Ghatee, M.
- Venue
- Artificial Intelligence Review
- Published
- 2021-03-03
- Volume
- 54
- Issue
- 8
- Pages
- 6391-6438
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3134811368
- Cited by
- 573
- References
- 192
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A systematic review on overfitting control in shallow and deep neural networks. https://app.cochat.ai/papers/10.1007/s10462-021-09975-1
- The paper
- Bejani, M. M., & Ghatee, M. (2021). A systematic review on overfitting control in shallow and deep neural networks. Artificial Intelligence Review. https://doi.org/10.1007/s10462-021-09975-1
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.
