Making pretrained transformer attention sensitive to uncertainty during inference.
Using exact posterior probabilities to separate reducible model error from uncertainty in the data.
Accounting for ambiguous ground truth when comparing AI systems with experts.
Improving the reliability of fine-tuned foundation models with priors that account for uncertainty.
Adjusting dropout during inference to estimate uncertainty without additional labels or training.