Bin Shi

Hao Mei, Junxian Li, Zhiming Liang et al.

Traffic prediction is a crucial topic because of its broad scope of applications in the transportation domain. Recently, various studies have achieved promising results. However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations. In real-life scenarios, the deployment of sensors could be limited due to budget limitations and installation availability, which makes most current models not applicable. Though few pieces of literature tried to impute traffic states at the missing locations, these methods need the data simultaneously observed at the locations with sensors, making them not applicable to prediction tasks. Another drawback is the lack of measurement of uncertainty in prediction, making prior works unsuitable for risk-sensitive tasks or involving decision-making. To fill the gap, inspired by the previous inductive graph neural network, this work proposed an uncertainty-aware framework with the ability to 1) extend prediction to missing locations with no historical records and significantly extend spatial coverage of prediction locations while reducing deployment of sensors and 2) generate probabilistic prediction with uncertainty quantification to help the management of risk and decision making in the down-stream tasks. Through extensive experiments on real-life datasets, the result shows our method achieved promising results on prediction tasks, and the uncertainty quantification gives consistent results which highly correlated with the locations with and without historical data. We also show that our model could help support sensor deployment tasks in the transportation field to achieve higher accuracy with a limited sensor deployment budget.

Yuhang Liu, Yuefei Wu, Bin Shi et al.

Out-of-distribution (OOD) detection is critical to ensuring the reliability of deep learning applications and has attracted significant attention in recent years. A rich body of literature has emerged to develop efficient score functions that assign high scores to in-distribution (ID) samples and low scores to OOD samples, thereby helping distinguish OOD samples. Among these methods, distance-based score functions are widely used because of their efficiency and ease of use. However, deep learning often leads to a biased distribution of data features, and extreme features are inevitable. These extreme features make the distance-based methods tend to assign too low scores to ID samples. This limits the OOD detection capabilities of such methods. To address this issue, we propose a simple yet effective method, Feature Bank Enhancement (FBE), that uses statistical characteristics from dataset to identify and constrain extreme features to the separation boundaries, therapy making the distance between samples inside and outside the distribution farther. We conducted experiments on large-scale ImageNet-1k and CIFAR-10 respectively, and the results show that our method achieves state-of-the-art performance on both benchmark. Additionally, theoretical analysis and supplementary experiments are conducted to provide more insights into our method.