InfoAtlas: Foundation Model for Zero-Shot Statistical Dependence Estimation

A new arXiv paper introduces InfoAtlas, a foundation model designed to estimate statistical dependence between high-dimensional random variables without per-task training. The authors target the high computational cost of existing neural mutual information estimators, which usually rely on iterative optimization. The method aims to deliver zero-shot dependence estimates, potentially removing the need for task-specific tuning.