9 citations · 13 across the 10 of their papers we have counts for
10 papers
HonestAffinity: Leak-Aware Evaluation of Protein and Pocket Priors for Binding Affinity Prediction
Junhao Wei, Baili Lu, Zhenhong Peng +10
Sequence-based deep learning offers a scalable alternative to structure-based scoring for protein-ligand binding affinity prediction. However, progress is hard to interpret when ar…
Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector
Junhao Wei, Yanxiao Li, Bidong Chen +9
We present a unified experiment, analysis, and benchmark study of multivariate time-series (MTS) anomaly detection. Ten family-representative detectors -- spanning statistical, rec…
AeroTSBoost: Temporal-Statistical Boosting for Real-World UAV Telemetry Anomaly Mining
Junhao Wei, Haochen Li, Yanxiao Li +7
Mining anomalies from unmanned aerial vehicle (UAV) state-estimation logs is challenging because failures are sparse, temporally structured, and distributed across heterogeneous PX…
Landscape-Aware Bandit Hyper-Heuristics for Online Operator Selection in UAV Inspection Routing
Junhao Wei, Yanxiao Li, Yifu Zhao +8
UAV multi-site inspection often reduces to choosing a high-quality visiting order after target sites have been extracted from a map. This paper develops LA-BHH, a landscape-aware b…
WASHH: An Anchor-Aware Whale-Guided Selection Hyper-Heuristic for Continuous Optimization and SVC Configuration
Yifu Zhao, Xiaofan Zou, Junhao Wei +9
Learning-assisted algorithm design often has to make reliable search decisions under small evaluation budgets, where committing to a single metaheuristic can be unreliable. We prop…
Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling
Junhao Wei, Yanxiao Li, Yifu Zhao +8
Learning-assisted hyper-heuristics can select among dispatching rules while preserving the feasibility and interpretability of constructive Job Shop Scheduling Problem (JSSP) heuri…
Reasoning or Not? A Comprehensive Evaluation of Reasoning LLMs for Dialogue Summarization
Keyan Jin, Yapeng Wang, Leonel Santos +4
Dialogue summarization is a challenging task with significant practical value in customer service, meeting analysis, and conversational AI. Although large language models (LLMs) ha…
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
Chak Fong Chong, Jielong Guo, Xu Yang +2
Multi-label image classification datasets are often partially labeled where many labels are missing, posing a significant challenge to training accurate deep classifiers. However,…
Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labels
Chak Fong Chong, Xinyi Fang, Jielong Guo +4
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image. Assigning pseudo-labels to unknown labels to gain additional t…
Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification
Chak Fong Chong, Yapeng Wang, Benjamin Ng +2
Classification on smartphone-captured chest X-ray (CXR) photos to detect pathologies is challenging due to the projective transformation caused by the non-ideal camera position. Re…