3 citations · 9 across the 7 of their papers we have counts for
7 papers
STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction
Xunlian Luo, Chunjiang Zhu, Detian Zhang +1
Traffic prediction has been an active research topic in the domain of spatial-temporal data mining. Accurate real-time traffic prediction is essential to improve the safety, stabil…
Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
Haiyang Liu, Chunjiang Zhu, Detian Zhang +1
Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. The key challenge is to effectively model complex spatial-tempora…
Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction
Xunlian Luo, Chunjiang Zhu, Detian Zhang +1
Accurate and real-time traffic state prediction is of great practical importance for urban traffic control and web mapping services. With the support of massive data, deep learning…
Heterogeneous Graph Sparsification for Efficient Representation Learning
Chandan Chunduru, Chun Jiang Zhu, Blake Gains +1
Graph sparsification is a powerful tool to approximate an arbitrary graph and has been used in machine learning over homogeneous graphs. In heterogeneous graphs such as knowledge g…
Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods
Guannan Liang, Qianqian Tong, Chunjiang Zhu +1
Stochastically controlled stochastic gradient (SCSG) methods have been proved to converge efficiently to first-order stationary points which, however, can be saddle points in nonco…
An Efficient Algorithm for Deep Stochastic Contextual Bandits
Tan Zhu, Guannan Liang, Chunjiang Zhu +2
In stochastic contextual bandit (SCB) problems, an agent selects an action based on certain observed context to maximize the cumulative reward over iterations. Recently there have…
Asynchronous Parallel Stochastic Quasi-Newton Methods
Qianqian Tong, Guannan Liang, Xingyu Cai +2
Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-…