AI RESEARCH
InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate
arXiv CS.AI
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ArXi:2606.00241v1 Announce Type: cross Measuring statistical dependency between high-dimensional random variables is a fundamental task in data science and machine learning. Neural mutual information (MI) estimators offer a promising avenue, but they typically require costly iterative optimization for each new dataset, making them impractical for real-time applications. We present InfoAtlas, a foundation model-like architecture that eliminates this bottleneck by directly inferring MI in a single forward pass.