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The paper proposes PoSE, a Bayesian distribution system state estimation method that uses a flow-matching-trained neural network to learn only the distribution of substation injections, while power flow equations and measurements enter at sampling time via a noise-aware Kalman-type correction, achieving better CRPS and calibrated uncertainty than Gaussian priors and supervised estimators on IEEE 33-bus and Iowa 240-bus feeders.
该论文提出了多层粒子边缘Metropolis-Hastings（MLPMMH）方法，通过伪边缘推断与跨网络宽度截断层级的耦合SMC相结合，实现分层贝叶斯深度学习中参数与超参数的联合推断，并在理论上证明了O(ε²)的均方误差与O(ε⁻²)的计算复杂度，同时给出可扩展的近似版本S-MLPMMH用于MNIST实验。
The paper introduces a unified Bayesian variational framework combining spike-and-slab sparsity and Gaussian mixture quantization for high compression rates in large neural networks with minimal accuracy loss.
This paper shows that Monte Carlo dropout provides epistemic uncertainty signals for chest radiograph classifiers, which improves error detection and reduces confident misdiagnoses in clinical decision-support agents when communicated as a binary error-risk flag.
This paper develops a Bayesian deep learning framework to estimate the causal effect of air pollution regulations on PM2.5 concentrations in London from 2010 to 2020, finding an average reduction of 1.88 μg/m³ (12.35%).
This position paper argues that sampling-based inference in Bayesian neural networks has achieved computational parity with optimization-based methods and is poised to supersede them, offering superior uncertainty quantification and prediction performance.
bde is a Python package that brings sampling-based Bayesian Deep Learning to practitioners via the MILE method, combining JAX's speed with scikit-learn's API for tabular supervised learning tasks.