English(EN) When Has a Bayesian Neural Network Sampled Enough? Adaptive Inference Time with Statistical Guarantees

贝叶斯神经网络实现具有统计保证的自适应推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种使用置信序列的新颖方法，以动态确定贝叶斯神经网络预测所需的蒙特卡洛样本数量。该方法通过在达到所需精度的决策后停止采样来确保统计保证，这与传统的固定样本方法不同。实验表明，这种自适应策略可以有效地分配计算资源，为模糊的输入分配更多样本，并降低整体延迟。 AI

影响这项研究通过优化贝叶斯模型中的计算资源分配，有望实现更高效、更可靠的 AI 决策。

排序理由关于贝叶斯神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Fabian Denoodt, Sibylle Hess · 2026-10-09 04:00

贝叶斯神经网络何时采样足够？具有统计保证的自适应推理时间

arXiv:2610.12212v1 Announce Type: new Abstract: Bayesian neural network predictions are commonly approximated using a fixed number of Monte Carlo samples per input, without controlling the resulting error that comes from this finite sample. We propose the use of confidence sequen…