The DOI matches its Crossref record; 199 of the 199 references it cites were found in a registry or an index.
Application of deep learning algorithms in geotechnical engineering: a short critical review
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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
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
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
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Cited by
Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023) · Computer Science & IT Research Journal · doi:10.51594/csitrj.v4i3.629 · Cited by 172
Identification of Rare Wildlife in the Field Environment Based on the Improved YOLOv5 Model
Su, X., Zhang, J., Ma, Z., Dong, Y., Zi, J., Xu, N., Zhang, H., Xu, F., & Chen, F. (2024) · Remote Sensing · doi:10.3390/rs16091535 · Cited by 21
Metadata
- Authors
- Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., & Ding, X.
- Venue
- Artificial Intelligence Review
- Published
- 2021-02-16
- Volume
- 54
- Issue
- 8
- Pages
- 5633-5673
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3132291851
- Cited by
- 538
- References
- 199
- FWCI
- 68.56
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Application of deep learning algorithms in geotechnical engineering: a short critical review. https://app.cochat.ai/papers/10.1007/s10462-021-09967-1
- The paper
- Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., & Ding, X. (2021). Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-021-09967-1
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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.
