The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Flat Minima
Abstract
We present a new algorithm for finding low-complexity neural networks with high generalization capability. The algorithm searches for a “flat” minimum of the error function. A flat minimum is a large connected region in weight space where the error remains approximately constant. An MDL-based, Bayesian argument suggests that flat minima correspond to “simple” networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require gaussian assumptions and does not depend on a “good” weight prior. Instead we have a prior over input output functions, thus taking into account net architecture and training set. Although our algorithm requires the computation of second-order derivatives, it has backpropagation's order of complexity. Automatically, it effectively prunes units, weights, and input lines. Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms conventional backprop, weight decay, and “optimal brain surgeon/optimal brain damage.”
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Cites
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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
Self-citationREBUS and the anarchic brain: Toward a unified model of the brain action of psychedelics
Carhart-Harris, R. L., & Friston, K. J. (2019) · Pharmacological Reviews · doi:10.1124/pr.118.017160 · Cited by 1,180
A Comprehensive Survey of Continual Learning: Theory, Method and Application
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Interpretable machine learning: Fundamental principles and 10 grand challenges
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Metadata
- Authors
- Hochreiter, S., & Schmidhuber, J.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 1997-01-01
- Volume
- 9
- Issue
- 1
- Pages
- 1-42
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2912811302
- Cited by
- 676
- References
- 42
- FWCI
- 11.41
- Type
- journal article
- Language
- en
- Open access
- closed
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