3 papers
cs.CV2026
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
Jungwook Seo, Minjeong Kim, Younkwan Lee +2
Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solv…
cs.LG2026
Sparse Bayesian Message Passing under Structural Uncertainty
Yoonhyuk Choi, Jiho Choi, Chanran Kim +5
Semi-supervised learning on real-world graphs is frequently challenged by heterophily, where the observed graph is unreliable or label-disassortative. Many existing graph neural ne…
cs.LG2025
Edge-boosted graph learning for functional brain connectivity analysis
David Yang, Mostafa Abdelmegeed, John Modl +1
Predicting disease states from functional brain connectivity is critical for the early diagnosis of severe neurodegenerative diseases such as Alzheimer's Disease and Parkinson's Di…