The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
An iterative pruning algorithm for feedforward neural networks
Abstract
The problem of determining the proper size of an artificial neural network is recognized to be crucial, especially for its practical implications in such important issues as learning and generalization. One popular approach for tackling this problem is commonly known as pruning and it consists of training a larger than necessary network and then removing unnecessary weights/nodes. In this paper, a new pruning method is developed, based on the idea of iteratively eliminating units and adjusting the remaining weights in such a way that the network performance does not worsen over the entire training set. The pruning problem is formulated in terms of solving a system of linear equations, and a very efficient conjugate gradient algorithm is used for solving it, in the least-squares sense. The algorithm also provides a simple criterion for choosing the units to be removed, which has proved to work well in practice. The results obtained over various test problems demonstrate the effectiveness of the proposed approach.
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Structured Pruning of Deep Convolutional Neural Networks
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Metadata
- Authors
- Castellano, G., Fanelli, A. M., & Pelillo, M.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1997-05
- Volume
- 8
- Issue
- 3
- Pages
- 519-531
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2126333738
- Cited by
- 295
- References
- 41
- FWCI
- 8.56
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
