LTE-ODE repurposes local truncation error as an unsupervised dynamic attention mask that preserves continuous Neural ODE evolution in stable regions while triggering discrete compensation only at anomaly points in large-scale traffic data.
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Multi-Beholder integrates one-class classification into multiple instance learning to predict LGG biomarker status from histopathology images, reporting AUCs of 0.973 on TCGA-LGG and 0.820 on an external Xiangya cohort.
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.
citing papers explorer
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Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
LTE-ODE repurposes local truncation error as an unsupervised dynamic attention mask that preserves continuous Neural ODE evolution in stable regions while triggering discrete compensation only at anomaly points in large-scale traffic data.
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Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification
Multi-Beholder integrates one-class classification into multiple instance learning to predict LGG biomarker status from histopathology images, reporting AUCs of 0.973 on TCGA-LGG and 0.820 on an external Xiangya cohort.
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Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.