1.2k citations · 2.4k across the 30 of their papers we have counts for
14 papers · 1 filter
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning
Weijia Zhang, Hao Liu, Fan Wang +4
Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drive…
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation
Carter Blum, Hao Liu, Hui Xiong
Electric vehicles have been rapidly increasing in usage, but stations to charge them have not always kept up with demand, so efficient routing of vehicles to stations is critical t…
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks
Jindong Han, Hao Liu, Hengshu Zhu +2
Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weat…
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…
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…
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,…
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…
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…
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
Weijia Zhang, Hao Liu, Yanchi Liu +2
The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction…
A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan +5
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this wa…
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Liang Wu, Hui Xiong, Liang Du +6
The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been…