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Learning rate schedules for faster stochastic gradient search
Abstract
The authors propose a new methodology for creating the first automatically adapting learning rates that achieve the optimal rate of convergence for stochastic gradient descent. Empirical tests agree with theoretical expectations that drift can be used to determine whether the crucial parameter c is large enough. Using this statistic, it will be possible to produce the first adaptive learning rates which converge at optimal speed.>
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Cited by
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Kim, J., Calhoun, V. D., Shim, E., & Lee, J.-H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.018 · Cited by 354
Metadata
- Authors
- Darken, C., Chang, J., & Moody, J.
- Venue
- Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop
- Publisher
- IEEE
- Published
- 2003-01-02
- Pages
- 3-12
- OpenAlex
- W2534068475
- Cited by
- 212
- References
- 0
- FWCI
- 3.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Learning rate schedules for faster stochastic gradient search. https://app.cochat.ai/papers/10.1109/nnsp.1992.253713
- The paper
- Darken, C., Chang, J., & Moody, J. (2003). Learning rate schedules for faster stochastic gradient search. Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop. https://doi.org/10.1109/nnsp.1992.253713
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