The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
On the problem of local minima in backpropagation
Abstract
The authors propose a theoretical framework for backpropagation (BP) in order to identify some of its limitations as a general learning procedure and the reasons for its success in several experiments on pattern recognition. The first important conclusion is that examples can be found in which BP gets stuck in local minima. A simple example in which BP can get stuck during gradient descent without having learned the entire training set is presented. This example guarantees the existence of a solution with null cost. Some conditions on the network architecture and the learning environment that ensure the convergence of the BP algorithm are proposed. It is proven in particular that the convergence holds if the classes are linearly separable. In this case, the experience gained in several experiments shows that multilayered neural networks (MLNs) exceed perceptrons in generalization to new examples.>
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Citations
Cites
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
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Phoneme recognition using time-delay neural networks
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.21701 · Cited by 2,643
Generalization of back-propagation to recurrent neural networks
Pineda, F. J. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.2229 · Cited by 931
Cited by
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Pollino, C. A., Woodberry, O., Nicholson, A., Korb, K., & Hart, B. T. (2007) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2006.03.006 · Cited by 477
Grossberg, S. (2013) · Neural Networks · doi:10.1016/j.neunet.2012.09.017 · Cited by 410
Let a biogeography-based optimizer train your Multi-Layer Perceptron
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.038 · Cited by 329
Computational Capabilities of Graph Neural Networks
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005141 · Cited by 225
Self-citationMattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., Tang, H., Wei, G.-Y., Bailis, P., Bittorf, V., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.01500 · Cited by 206
“DNN training involves many stochastic influences that manifest in substantial run-to-run variation (Choromanska et al., 2015; Gori & Tesi, 1992; Auer et al., 1996; Coleman et al., 2019 selection and the non-commutative nature of floating-point addition).”
Background
Metadata
- Authors
- Gori, M., & Tesi, A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1992
- Volume
- 14
- Issue
- 1
- Pages
- 76-86
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2022740958
- Cited by
- 650
- References
- 4
- FWCI
- 13.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gori, M., & Tesi, A. (1992). On the problem of local minima in backpropagation. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.107014
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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