709 citations · 834 across the 10 of their papers we have counts for
10 papers
Meta Attentive Graph Convolutional Recurrent Network for Traffic Forecasting
Adnan Zeb, Yongchao Ye, Shiyao Zhang +1
Traffic forecasting is a fundamental problem in intelligent transportation systems. Existing traffic predictors are limited by their expressive power to model the complex spatial-t…
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
Yuanshao Zhu, Yongchao Ye, Shiyao Zhang +2
Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal…
Adaptive Modeling of Uncertainties for Traffic Forecasting
Ying Wu, Yongchao Ye, Adnan Zeb +2
Deep neural networks (DNNs) have emerged as a dominant approach for developing traffic forecasting models. These models are typically trained to minimize error on averaged test cas…
Traffic Prediction with Transfer Learning: A Mutual Information-based Approach
Yunjie Huang, Xiaozhuang Song, Yuanshao Zhu +2
In modern traffic management, one of the most essential yet challenging tasks is accurately and timely predicting traffic. It has been well investigated and examined that deep lear…
Uncertainty Quantification for Traffic Forecasting: A Unified Approach
Weizhu Qian, Dalin Zhang, Yan Zhao +2
Uncertainty is an essential consideration for time series forecasting tasks. In this work, we specifically focus on quantifying the uncertainty of traffic forecasting. To achieve t…
Social-DualCVAE: Multimodal Trajectory Forecasting Based on Social Interactions Pattern Aware and Dual Conditional Variational Auto-Encoder
Jiashi Gao, Xinming Shi, James J. Q. Yu
Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems. The future trajectory forecast…
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis
Yi Liu, Yuanshao Zhu, James J. Q. Yu
Efficient collaboration between collaborative machine learning and wireless communication technology, forming a Federated Edge Learning (FEEL), has spawned a series of next-generat…
Complicating the Social Networks for Better Storytelling: An Empirical Study of Chinese Historical Text and Novel
Chenhan Zhang
Digital humanities is an important subject because it enables developments in history, literature, and films. In this paper, we perform an empirical study of a Chinese historical t…
Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Yi Liu, James J. Q. Yu, Jiawen Kang +2
Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of datasets gathered by governments and organizations. Howeve…
PPGAN: Privacy-preserving Generative Adversarial Network
Yi Liu, Jialiang Peng, James J. Q Yu +1
Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality gene…