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Found 3 papers, 2 papers with code
Synthetic data generation for system identification: leveraging knowledge transfer from similar systems
This paper addresses the challenge of overfitting in the learning of dynamical systems by introducing a novel approach for the generation of synthetic data, aimed at enhancing model generalization and robustness in scenarios characterized by data scarcity.
Gradient-based bilevel optimization for multi-penalty Ridge regression through matrix differential calculus
Common regularization algorithms for linear regression, such as LASSO and Ridge regression, rely on a regularization hyperparameter that balances the tradeoff between minimizing the fitting error and the norm of the learned model coefficients.
Split-Boost Neural Networks
The calibration and training of a neural network is a complex and time-consuming procedure that requires significant computational resources to achieve satisfactory results.
