Scalable Bayesian Gaussian Process Classification for cancer histopathology: calibrated uncertainty under distribution shift
Read the original at arxiv.org→arXiv:2611.07127v1 Announce Type: new Abstract: Reliable uncertainty quantification is essential for clinical deployment of cancer diagnosis models, yet neural networks and variational Gaussian process methods can...
Original headline: "Scalable Bayesian Gaussian Process Classification for Cancer Histopathology: Calibrated Uncertainty Under Distribution Shift"
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- Nov 6, 05:00 UTC arXiv cs.LG lead source Scalable Bayesian Gaussian Process Classification for Cancer Histopathology: Calibrated Uncertainty Under Distribution Shift