1.2k citations · 2.4k across the 30 of their papers we have counts for
12 papers · 1 filter
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
Le Yu, Leilei Sun, Bowen Du +3
Heterogeneous graphs are pervasive in practical scenarios, where each graph consists of multiple types of nodes and edges. Representation learning on heterogeneous graphs aims to o…
Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction
Jingbo Zhou, Shuangli Li, Liang Huang +5
Accurately predicting the binding affinity between drugs and proteins is an essential step for computational drug discovery. Since graph neural networks (GNNs) have demonstrated re…
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
Junchen Ye, Leilei Sun, Bowen Du +2
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station…
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng +4
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a h…
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
Wei Fan, Kunpeng Liu, Hao Liu +3
We study the problem of balancing effectiveness and efficiency in automated feature selection. After exploring many feature selection methods, we observe a computational dilemma: 1…
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
Denghui Zhang, Junming Liu, Hengshu Zhu +4
Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for…
Learning Adaptive Embedding Considering Incremental Class
Yang Yang, Zhen-Qiang Sun, HengShu Zhu +3
Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning,…
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
Jingbo Zhou, Zhenwei Tang, Min Zhao +6
A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile…
S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification
Yang Yang, Zhen-Qiang Sun, Hui Xiong +1
Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples…
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
Hao Liu, Ying Li, Yanjie Fu +4
Public transportation plays a critical role in people's daily life. It has been proven that public transportation is more environmentally sustainable, efficient, and economical tha…
Predicting Temporal Sets with Deep Neural Networks
Le Yu, Leilei Sun, Bowen Du +3
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In pr…
Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling
Mu Yuan, Lan Zhang, Xiang-Yang Li +1
Labeling data (e.g., labeling the people, objects, actions and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were propos…