New Neighborhood Smoothing method improves neural network calibration

Researchers have introduced a novel approach called Neighborhood Smoothing for Calibration, designed to address the overconfidence issue in modern neural networks. This method leverages the neighborhood structure within learned representations, encouraging similar predictive distributions for nearby samples. The technique, implemented as a graph-based regularizer, penalizes divergence between neighboring predictive distributions and has shown empirical improvements in predictive quality and calibration across various benchmarks. AI

IMPACT This research offers a new train-time calibration technique that could lead to more reliable and trustworthy neural network predictions.

RANK_REASON Academic paper detailing a new method for neural network calibration. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• graph smoothing
• Hugging Face
• Jensen-Shannon divergence
• Neighborhood Smoothing for Calibration
• Neural Networks

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →