Introducing TabH2O

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Introduces tabH2O, a foundation model for tabular data. Details its dual-head architecture for unified training, single-stage pretraining with stability improvements (bounded scalable softmax, inter-stage normalization, learnable residual scaling, logit soft-capping), and noise-aware pretraining for robustness. Highlights its ability to perform classification and regression with in-context learning, outperforming classical ML methods without explicit training or feature engineering.