141 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN7 papers
- Shandong UniversityCN4 papers
- University of Chinese Academy of SciencesCN4 papers
- George Mason UniversityUS3 papers
- Peking UniversityCN3 papers
- Southwest UniversityCN3 papers
- Wuhan UniversityCN3 papers
- Academy of Mathematics and Systems ScienceCN2 papers
- Beihang UniversityCN2 papers
- Harbin Institute of TechnologyCN2 papers
- Tsinghua UniversityCN2 papers
- University of Hong KongHK2 papers
9 papers · 1 filter
RegionGCN: Spatial-Heterogeneity-Aware Graph Convolutional Networks
Hao Guo, Han Wang, Di Zhu +3
Modeling spatial heterogeneity in the data generation process is essential for understanding and predicting geographical phenomena. Despite their prevalence in geospatial tasks, ne…
Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction
Hao Zhang, Yang Liu, Xiaoyan Liu +4
We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and impor…
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable
Cambridge Yang, Michael Littman, Michael Carbin
In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal p…
Partial Multi-label Learning with Label and Feature Collaboration
Tingting Yu, Guoxian Yu, Jun Wang +1
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant. The PML p…
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Yuying Xing, Guoxian Yu, Carlotta Domeniconi +3
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple…
Ranking-based Deep Cross-modal Hashing
Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi +3
Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are…
Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models
Xiaolei Ma, Yi Li, Zhiyong Cui +1
Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies o…
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Haiyang Yu, Zhihai Wu, Shuqin Wang +2
Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in whi…
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Xiaolei Ma, Zhuang Dai, Zhengbing He +3
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spati…