Accuracy estimation of neural networks by extreme value theory

The article explores the application of extreme value theory to estimate the error in neural networks, which are recognized for their strong capability to approximate continuous functions. By concentrating on large error values, the research seeks to better quantify the bias between the true function and the neural network's output. This approach aims to improve the practical utility of neural networks by providing more accurate error estimations. The study highlights how focusing on extreme errors can yield insights that traditional average-based error metrics might overlook. Such advancements could enhance the reliability and interpretability of neural network predictions in various applications. This research aligns with ongoing efforts to refine neural network performance assessment, as indicated by related recent studies. Overall, the integration of extreme value theory offers a promising direction for advancing neural network accuracy estimation.