The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Gradient descent learning algorithm overview: a general dynamical systems perspective
Abstract
Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning.
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Citations
Cites
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578
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
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks
Xia, M., Li, T., Xu, L., Liu, L., & de Silva, C. W. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2728371 · Cited by 723
Lusci, A., Pollastri, G., & Baldi, P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400187y · Cited by 555
Self-citationAn analog VLSI recurrent neural network learning a continuous-time trajectory
Cauwenberghs, G. (1996) · IEEE Transactions on Neural Networks · doi:10.1109/72.485671 · Cited by 127
Physical human locomotion prediction using manifold regularization
Javeed, M., Shorfuzzaman, M., Alsufyani, N., Chelloug, S. A., Jalal, A., & Park, J. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1105 · Cited by 18
Metadata
- Authors
- Baldi, P.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1995
- Volume
- 6
- Issue
- 1
- Pages
- 182-195
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2124972241
- Cited by
- 258
- References
- 4
- FWCI
- 8.62
- Type
- journal article
- Language
- en
- Open access
- closed
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- Baldi, P. (1995). Gradient descent learning algorithm overview: a general dynamical systems perspective. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.363438
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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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