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LLM-Detector achieves superior anomaly detection in tabular data without the need for fine-tuning, making it a game-changer for real-world applications.
Transporting causal effects across different network populations can significantly enhance intervention strategies in social networks and public health.
Tabular anomaly detection gets a serious upgrade: uLEAD-TabPFN leverages frozen PFNs to model complex feature dependencies, outperforming existing methods by a significant margin, especially in high-dimensional spaces.
