AINeutralarXiv – CS AI · 6h ago6/10
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When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning
Researchers introduce Adaptive Binning, a self-supervised learning method for medical tabular data that dynamically adjusts feature discretization during training rather than using fixed global quantization. The approach combines curriculum learning with representation-aware binning to improve performance on unlabeled clinical datasets, alongside a new standardized benchmark for medical tabular SSL evaluation.