提出保结构校正学习方法，提升脑源成像中稀疏贝叶斯推断的精度与可靠性。
arXiv:2606.07196v1 Announce Type: new Abstract: Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source a…
贝叶斯张量分解结合扩散模型先验，为高维数据填补与推断提供新思路，方法创新且适用于多领域。
arXiv:2606.03212v1 Announce Type: new Abstract: Low-rank tensor decomposition (TD) is usually effective on clean, fully observed data, but it often de…
探讨仅用最后一层进行不确定性量化是否足够，挑战主流认知，对模型可靠部署有重要启发
arXiv:2605.30741v1 Announce Type: cross Abstract: Epistemic uncertainty quantification (UQ) for deep neural networks (DNNs) is a requirement for safe …
揭秘大模型内部贝叶斯推断的几何结构，从小模型到生产级LLM的规模扩展规律
arXiv:2512.23752v5 Announce Type: replace Abstract: Recent work has shown that small transformers trained in controlled "wind-tunnel'' settings can im…
将摊销思想引入能量基贝叶斯推断，用传输方法一键解决非线性反问题，告别逐次MCMC的昂贵计算。
arXiv:2605.15407v1 Announce Type: cross Abstract: We consider amortized Bayesian inference for nonlinear inverse problems in settings where only sampl…
用元学习+贝叶斯推断搞定复杂系统根因分析，无需完整因果图即可高效定位故障。
arXiv:2605.08786v2 Announce Type: replace Abstract: Root cause analysis (RCA) in complex systems is challenging due to error propagation across multip…