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arXiv Machine Learning By Taiga Saito, Yu Otake, Daijiro Mizutani, Stephen Wu

TabPFN Extensions for Interpretable Geotechnical Modelling

Read the original on arXiv Machine Learning →

arXiv:2603. 21033v3 Announce Type: replace-cross Abstract: Geotechnical site characterisation relies on sparse, heterogeneous borehole data, where uncertainty quantification and interpretability matter as much as predictive accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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