1 citations · 1 across the 9 of their papers we have counts for
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ColdDDI: Evaluating Knowledge Utilization in Cold-Start Drug-Drug Interaction Prediction
Jiheng Liang, Chen Zhao, Di Wu +4
Cold-start drug-drug interaction (DDI) prediction tests whether models can identify clinically significant interactions for drugs without training-time interaction history. Existin…
Reducing Learner Redundancy in Boosting via Residual Orthogonalization
Ye Su, Jipeng Guo, Xin Xu +4
While sequential residual fitting is the bedrock of standard boosting frameworks, it inherently breeds learner redundancy by repeatedly revisiting correlated error components. To a…
Implicit Fine-tuning via Context Engineering: A Curriculum Learning Framework for Multimodal Entity Alignment
Yunpeng Hong, Chenyang Bu, Di Wu +2
Multimodal Entity Alignment (MMEA) aims to identify equivalent entities across different modalities. While existing methods enhance MMEA performance through black-box context engin…
Full spectrum Unlearnable Examples via Spectral Equalization
Jiale Cai, Gezheng Xu, Zhihao Li +6
Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In this paper, we reveal that…
FLFL: Federated Latent Factor Learning for Private Recovery of Spatio-Temporal Signals
Chengjun Yu, Di Wu, Yi He +1
Wireless sensor network (WSNs) stands out as a burgeoning and promising domain in intelligent sensing. Owing to various factors such as sudden sensor malfunctions or deliberate shu…
DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delay
Yulin Wang, Yi He, Dianlong You +1
Online learning is crucial for handling complex data streams in big data applications. Recent research has begun to focus on dynamic scenarios, i.e., non-stationary environments. H…
PSQE: A Theoretical-Practical Approach to Pseudo Seed Quality Enhancement for Unsupervised Multimodal Entity Alignment
Yunpeng Hong, Chenyang Bu, Jie Zhang +3
Multimodal Entity Alignment (MMEA) aims to identify equivalent entities across different data modalities, enabling structural data integration that in turn improves the performance…