activity
20202026
most citedRobust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation

10 citations · 14 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2026

SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition

Jun Zhuang, Mohammad Al Hasan, Yiyu Shi +1

Noisy-label learning in small-scale medical image classification is challenging and hinders the superiority of deep neural networks. Recent studies suggest that quantum neural netw…

cs.CV2026

PaQ-DETR: Learning Pattern and Quality-Aware Dynamic Queries for Object Detection

Zhengjian Kang, Jun Zhuang, Kangtong Mo +3

Detection Transformer (DETR) has redefined object detection by casting it as a set prediction task within an end-to-end framework. Despite its elegance, DETR and its variants still…

cs.LG2023★ 3 cited

Robust Node Representation Learning via Graph Variational Diffusion Networks

Jun Zhuang, Mohammad Al Hasan

Node representation learning by using Graph Neural Networks (GNNs) has been widely explored. However, in recent years, compelling evidence has revealed that GNN-based node represen…

cs.LG2022★ 10 cited

Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation

Jun Zhuang, Mohammad Al Hasan

Node classification using Graph Neural Networks (GNNs) has been widely applied in various real-world scenarios. However, in recent years, compelling evidence emerges that the perfo…

cs.LG2022

Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision

Jun Zhuang, Mohammad Al Hasan

In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task. However, GCNs may be…

cs.LG2021★ 1 cited

Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks

Jun Zhuang, Mohammad Al Hasan

Supervised learning, while deployed in real-life scenarios, often encounters instances of unknown classes. Conventional algorithms for training a supervised learning model do not p…

cs.LG2020

Deperturbation of Online Social Networks via Bayesian Label Transition

Jun Zhuang, Mohammad Al Hasan

Online social networks (OSNs) classify users into different categories based on their online activities and interests, a task which is referred as a node classification task. Such…

eess.IV2020

Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks

Jun Zhuang, Dali Wang

Microscopic images from multiple modalities can produce plentiful experimental information. In practice, biological or physical constraints under a given observation period may pre…