Uncertainty Without the Sampling Tax
TL;DR for operators Many production AI systems do not need a more poetic answer. They need a cheaper way to decide whether the answer should be trusted at all. The paper introduces Calibrated Variance Propagation (CVP), a test-time method for Bayesian deep learning that estimates predictive uncertainty without repeatedly sampling model weights through many forward passes.1 It targets a practical bottleneck: recent variational training methods can now produce Gaussian weight posteriors for large neural networks at training costs comparable to standard optimizers, but using those posteriors at inference usually means Monte Carlo sampling. That is expensive, especially when the model must respond in real time. Apparently, reliability is still expected to fit inside latency budgets. Outrageous. ...