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Upper generalization bounds for neural oscillators
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DOI：10.1016/j.probengmech.2026.103977.png)
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Abstract
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• This study establishes upper generalization bounds for neural oscillators. • Estimation errors grow polynomially with respect to MLP sizes and the time length. • Regularizing MLP Lipschitz constants improves the generalization of the neural oscillator.
Keywords:
Neural oscillator
PAC generalization bound
Causal continuous operator
Rademacher complexity
Wasserstein-1 distance
Lipschitz regularization
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