activity
20182021
most citedPredicting Temporal Sets with Deep Neural Networks

54 citations · 89 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2021

Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning

Dongjie Wang, Kunpeng Liu, Pauline Johnson +3

Urban planning designs land-use configurations and can benefit building livable, sustainable, safe communities. Inspired by image generation, deep urban planning aims to leverage d…

cs.LG20212 cited

Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data

Xuyan Tan, Yuhang Wang, Bowen Du +4

Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this lin…

cs.LG20208 cited

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…

cs.LG202023 cited

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…

cs.CR20202 cited

Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection

Dongjie Wang, Pengyang Wang, Jingbo Zhou +3

While Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. Effective detection of attacks can…

cs.LG202054 cited

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…