24 citations · 92 across the 36 of their papers we have counts for
6 papers · 1 filter
Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation
Tsung-Han Wu, Hung-Ting Su, Shang-Tse Chen +1
The robust self-training (RST) framework has emerged as a prominent approach for semi-supervised adversarial training. To explore the possibility of tackling more complicated tasks…
Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach
ChungYi Lin, Shen-Lung Tung, Hung-Ting Su +1
Traditional traffic prediction, limited by the scope of sensor data, falls short in comprehensive traffic management. Mobile networks offer a promising alternative using network ac…
TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
ChungYi Lin, Shen-Lung Tung, Hung-Ting Su +1
To address the limitations of traffic prediction from location-bound detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensiv…
Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change
Chien Cheng Chyou, Hung-Ting Su, Winston H. Hsu
Adversarial robustness poses a critical challenge in the deployment of deep learning models for real-world applications. Traditional approaches to adversarial training and supervis…
Fair Robust Active Learning by Joint Inconsistency
Tsung-Han Wu, Hung-Ting Su, Shang-Tse Chen +1
Fairness and robustness play vital roles in trustworthy machine learning. Observing safety-critical needs in various annotation-expensive vision applications, we introduce a novel…
Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic
Chung-Yi Lin, Hung-Ting Su, Shen-Lung Tung +1
Spatial-temporal prediction is a critical problem for intelligent transportation, which is helpful for tasks such as traffic control and accident prevention. Previous studies rely…