most citedSequential edge detection using joint hierarchical Bayesian learning

2 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2023

Efficiently Visualizing Large Graphs

Xinyu Li, Yao Xiao, Yuchen Zhou

Most existing graph visualization methods based on dimension reduction are limited to relatively small graphs due to performance issues. In this work, we propose a novel dimension…

stat.AP2023

Exploring assessment method of technological advancement based on literature cross-citation

Shengxuan Tang, Liming Zhang, Shuo Jiang +2

Assessing advancements of technology is essential for creating science and technology policies and making informed investments in the technology market. However, current methods pr…

stat.AP20232 cited

Sequential edge detection using joint hierarchical Bayesian learning

Yao Xiao, Anne Gelb, Guohui Song

This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new metho…

cs.LG2023

Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks

Yao Xiao, Ji Xu, Jing Yang +1

Graph Convolutional Networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data. However, t…

cs.AI20221 cited

Semi-supervised Learning with Deterministic Labeling and Large Margin Projection

Ji Xu, Gang Ren, Yao Xiao +2

The centrality and diversity of the labeled data are very influential to the performance of semi-supervised learning (SSL), but most SSL models select the labeled data randomly. Th…

cs.CV20222 cited

Contributions of Shape, Texture, and Color in Visual Recognition

Yunhao Ge, Yao Xiao, Zhi Xu +2

We investigate the contributions of three important features of the human visual system (HVS)~ -- ~shape, texture, and color ~ -- ~to object classification. We build a humanoid vis…