Model Detail
tabpfn_2_5
—uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN
arXiv:2604.20255v1 Announce Type: new Abstract: Anomaly detection in tabular data is challenging due to high dimensionality, complex feature dependencies, and heterogeneous noise. Many existing methods rely on proximity-based cues and may miss anomalies caused by violations of complex feature depend
Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms
arXiv:2604.04868v2 Announce Type: replace-cross Abstract: Tabular foundation models (TFMs) such as TabPFN (Tabular Prior-Data Fitted Network) are designed to generalize across heterogeneous tabular datasets through in-context learning (ICL). They perform prediction in a single forward pass condition
TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts
arXiv:2510.06162v2 Announce Type: replace Abstract: Revealing novel insights from the relationship between molecular measurements and pathology remains a very impactful application of machine learning in biomedicine. Data in this domain typically contain only a few observations but thousands of pote
