14 citations · 14 across the 3 of their papers we have counts for
12 papers
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data
Zhipeng Luo, Zhixing He, Jin Wang +4
Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giv…
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction
Manqing Dong, Chunguang Pan, Zhipeng Luo
Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works tr…
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
Manqing Dong, Feng Yuan, Lina Yao +2
A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handl…
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
Manqing Dong, Feng Yuan, Lina Yao +3
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation r…
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Xiang Zhang, Lina Yao, Manqing Dong +3
Objective: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the world's population. Most of t…
Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning
Xiang Zhang, Xiaocong Chen, Lina Yao +2
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e.g., computer version). However, a severe challenge faced by deep learning is the hi…