LatentNN Corrects Underestimation Bias in Neural Networks

This paper addresses a critical issue in machine learning, particularly in astronomical applications, where models often underestimate extreme values due to noisy input data. The introduction of LatentNN provides a practical solution by incorporating latent variables to correct for attenuation bias, leading to more accurate predictions in low signal-to-noise scenarios. The availability of code is a significant advantage.