5 papers
Structured Frequency-Domain Evidence for LLM-Based Time-Series Anomaly Detection
Jungwook Seo, Sangwon Son, Minjeong Kim +3
Time-series anomalies can appear not only as pointwise deviations but also as changes in recurring temporal structure, such as shifted periodicity or localized oscillatory fluctuat…
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…
Persistent Tri-State Message Passing
Yoonhyuk Choi, Jiho Choi, Chong-Kwon Kim +6
In stochastic message passing, an edge's sampled role changes the node states used to compute adaptive weights at later layers. Weight averaging therefore depends on whether edge r…
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…
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge
Markus D. Schirmer, Archana Venkataraman, Islem Rekik +23
Large, open-source consortium datasets have spurred the development of new and increasingly powerful machine learning approaches in brain connectomics. However, one key question re…