The DOI matches its Crossref record; 145 of the 146 references it cites were found in a registry or an index.
Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It is highly efficient since it is a globally ordered and locally random learning mechanism [7].”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
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
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
Research works printed but not linked: 2
- Broomhead, 1988, Multi-variable functional interpolation and adaptive networks, Complex Syst., 2, 321
- Kasun, 2013, Representational learning with ELMs for big data, IEEE Intell. Syst., 28, 31
Cited by
Sorted by citations
DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects
Zhang, Y., Zhang, H., Huang, Q., Han, Y., & Zhao, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122669 · Cited by 300
Zhang, J., Zhao, Y., Shone, F., Li, Z., Frangi, A. F., Xie, S. Q., & Zhang, Z. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3226860 · Cited by 195
Self-citationWang, Y., Liu, W., Liu, J., & Sun, C. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.01.007 · Cited by 186
Aslam, N., Rustam, F., Lee, E., Washington, P. B., & Ashraf, I. (2022) · IEEE Access · doi:10.1109/access.2022.3165621 · Cited by 138
An Appraisal of Incremental Learning Methods
Luo, Y., Yin, L., Bai, W., & Mao, K. (2020) · Entropy · doi:10.3390/e22111190 · Cited by 120
Metadata
- Authors
- Zhang, J., Li, Y., Xiao, W., & Zhang, Z.
- Affiliations
- Peking University (CN)
- Beijing Institute of Technology (CN)
- University of Science and Technology Beijing (CN)
- University of Leeds (GB)
- Corresponding author
- Xiao, Wendong
- Venue
- Journal of the Franklin Institute
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 357
- Issue
- 13
- Pages
- 8925-8955
- ISSN
- 0016-0032 (ISSN-L)
- OpenAlex
- W3040832198
- Cited by
- 179
- References
- 146
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 22 · 2023: 83 · 2022: 35 · 2021: 17 · 2020: 2
- FWCI
- 13.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Franklin Institute (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Ministry of Science and Technology of the People's Republic of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- extreme learning machine, random neural networks, universal approximation, deep learning training efficiency
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
