The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Neural Network Learning Without Backpropagation
Abstract
The method introduced in this paper allows for training arbitrarily connected neural networks, therefore, more powerful neural network architectures with connections across layers can be efficiently trained. The proposed method also simplifies neural network training, by using the forward-only computation instead of the traditionally used forward and backward computation.
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Citations
Cites
A direct adaptive method for faster backpropagation learning: the RPROP algorithm
Riedmiller, M., & Braun, H. (2002) · IEEE International Conference on Neural Networks · doi:10.1109/icnn.1993.298623 · Cited by 3,917
Cited by
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
Trends in extreme learning machines: A review
Huang, G., Huang, G.-B., Song, S., & You, K. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.001 · Cited by 1,732
Metadata
- Authors
- Wilamowski, B. M., & Yu, H.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2010-11
- Volume
- 21
- Issue
- 11
- Pages
- 1793-1803
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2102013737
- Cited by
- 195
- References
- 1
- FWCI
- 12.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Neural Network Learning Without Backpropagation. https://app.cochat.ai/papers/10.1109/tnn.2010.2073482
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- Wilamowski, B. M., & Yu, H. (2010). Neural Network Learning Without Backpropagation. IEEE Transactions on Neural Networks. https://doi.org/10.1109/tnn.2010.2073482
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.
