LatentNN Corrects Underestimation Bias in Neural Networks
Published:Dec 29, 2025 01:59
•1 min read
•ArXiv
Analysis
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.
Key Takeaways
- •Neural networks suffer from attenuation bias, leading to underestimation of extreme values.
- •LatentNN is a method that corrects this bias by jointly optimizing network parameters and latent input values.
- •The method is particularly effective in low signal-to-noise regimes, common in astronomical data.
- •Code is available for practical implementation.
Reference
“LatentNN reduces attenuation bias across a range of signal-to-noise ratios where standard neural networks show large bias.”