Unity Smoothing for Handling Inconsistent Evidence in Bayesian Networks and Unity Propagation for Faster Inference

集合传输平滑。第一部分：统一框架

Ensemble transport smoothing. Part I: Unified framework

本文从测度传输视角提出一个基于非线性传输映射的通用集合平滑框架，解决了传统平滑方法在非高斯设置下的不一致或计算成本过高问题，并证明标准Kalman型平滑算法为其特例。

Comprehensive Analysis of Over-smoothing in Graph Neural Networks from Markov Chains Perspective

The over-smoothing problem is an obstacle of developing deep graph neural network (GNN). Although many approaches to improve the over-smoothing problem have been proposed, there is still a lack of comprehensive understanding and conclusion of this problem. In this work, we analyze the over-smoothing problem from the Markov chain perspective. We focus on message passing of GNN and first establish a connection between GNNs and Markov chains on the graph. GNNs are divided into two classes of operator-consistent and operator-inconsistent based on whether the corresponding Markov chains are time-ho…

Hidden Cost of Randomized Smoothing

The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public. While immense interests were in either crafting adversarial attacks as a way to measure the robustness of neural networks or devising worst-case analytical robustness verification with guarantees, few methods could enjoy both scalability and robustness guarantees at the same time. As an alternative to these attempts, randomized smoothing adopts a different prediction rule that enables statistical robustness arguments which easily scale to large networks. However, in t…

采样引导的异质图神经网络与时序平滑用于可扩展纵向数据插补

Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation

提出SHT-GNN框架，通过异质图建模观测与协变量，结合受试者级采样和时序平滑，实现大规模纵向数据的高效插补，在ADNI等数据集上显著优于现有方法。