65 citations · 73 across the 5 of their papers we have counts for
8 papers
Generalizing Neural Networks by Reflecting Deviating Data in Production
Yan Xiao, Yun Lin, Ivan Beschastnikh +3
Trained with a sufficiently large training and testing dataset, Deep Neural Networks (DNNs) are expected to generalize. However, inputs may deviate from the training dataset distri…
Self-Checking Deep Neural Networks in Deployment
Yan Xiao, Ivan Beschastnikh, David S. Rosenblum +4
The widespread adoption of Deep Neural Networks (DNNs) in important domains raises questions about the trustworthiness of DNN outputs. Even a highly accurate DNN will make mistakes…
Directed Graph Convolutional Network
Zekun Tong, Yuxuan Liang, Changsheng Sun +2
Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data. However, the undirected graphs limit their appli…
Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
Yuxuan Liang, Kun Ouyang, Yiwei Wang +4
Citywide crowd flow analytics is of great importance to smart city efforts. It aims to model the crowd flow (e.g., inflow and outflow) of each region in a city based on historical…
Fine-Grained Urban Flow Inference
Kun Ouyang, Yuxuan Liang, Ye Liu +4
The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the num…
MMKG: Multi-Modal Knowledge Graphs
Ye Liu, Hui Li, Alberto Garcia-Duran +3
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KG…