HiPreNets progressively refines neural networks via residual learning and adaptive techniques to reduce both RMSE and L^∞ errors, outperforming standard networks on Feynman benchmarks and enabling fast high-dimensional ODE surrogates.
Gradient boosting neural networks: Grownet.arXiv2020
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This systematic survey reviews data balancing methods for imbalanced datasets and concludes that no single technique is universally superior, with choice depending on data traits, classifier, and metrics.
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Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods
This systematic survey reviews data balancing methods for imbalanced datasets and concludes that no single technique is universally superior, with choice depending on data traits, classifier, and metrics.