3 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD Classification
Boseong Kim, Haejun Chung, Ikbeom Jang
Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional connectivity (FC) or temporall…
CoHyFuse: Condition-wise Hypergraph Fusion with Global Connectome in Task-fMRI
Boseong Kim, Haejun Chung, Ikbeom Jang
Task-fMRI connectomes reveal state-dependent neural reconfigurations, yet conventional methods marginalize these signals by aggregating distinct conditions into static pairwise gra…
Let Confidence Change, Not the Prediction: Prediction-Preserving Repair for Post-hoc Calibration
Daehwan Kim, Haejun Chung, Ikbeom Jang
Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated top-1 prediction. Accuracy captures only the net effect of these chang…
Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling
Yujin Park, Haejun Chung, Ikbeom Jang
Pairwise comparison is the gold standard for subjective ranking tasks; however, exhaustive annotation requires a massive number of human comparisons (). While sorting-based…
When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning
Daehwan Kim, Haejun Chung, Ikbeom Jang
Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored because reliable labels often require costly expert adjudication, even…
Ordinal-Aware Calibration for Ordinal Classification
Daehwan Kim, Haejun Chung, Ikbeom Jang
Deep neural networks frequently produce overconfident, miscalibrated predictions. In ordinal classification, predictions must also adhere to a unimodal and order-consistent structu…