This article explores TabPFN, a transformer-based foundation model designed for small tabular datasets. Trained on millions of synthetic datasets generated via structural causal models, TabPFN learns to predict labels through in-context learning. It outperforms traditional methods like CatBoost and XGBoost in both speed and accuracy, while offering robustness, interpretability, and fine-tuning capabilities. A breakthrough in tabular ML, it redefines what's possible on structu

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SEDD redefines generative AI with human-like reasoning, enabling faster, high-quality text and code through discrete diffusion models.