26 citations · 41 across the 11 of their papers we have counts for
13 papers
Localization-Guided Foreground Augmentation in Autonomous Driving
Jiawei Yong, Deyuan Qu, Qi Chen +2
Autonomous driving systems often degrade under adverse visibility conditions-such as rain, nighttime, or snow-where online scene geometry (e.g., lane dividers, road boundaries, and…
TrajGPT-R: Generating Urban Mobility Trajectory with Reinforcement Learning-Enhanced Generative Pre-trained Transformer
Jiawei Wang, Chuang Yang, Jiawei Yong +6
Mobility trajectories are essential for understanding urban dynamics and enhancing urban planning, yet access to such data is frequently hindered by privacy concerns. This research…
Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecasting
Hongjun Wang, Jiawei Yong, Jiawei Wang +2
Traffic prediction remains a key challenge in spatio-temporal data mining, despite progress in deep learning. Accurate forecasting is hindered by the complex influence of external…
How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning
Haotian Gao, Zheng Dong, Jiawei Yong +3
Spatio-temporal forecasting is essential for real-world applications such as traffic management and urban computing. Although recent methods have shown improved accuracy, they ofte…
Graph Community Augmentation with GMM-based Modeling in Latent Space
Shintaro Fukushima, Kenji Yamanishi
This study addresses the issue of graph generation with generative models. In particular, we are concerned with graph community augmentation problem, which refers to the problem of…
Multimodal Point-of-Interest Recommendation
Yuta Kanzawa, Toyotaro Suzumura, Hiroki Kanezashi +2
Large Language Models are applied to recommendation tasks such as items to buy and news articles to read. Point of Interest is quite a new area to sequential recommendation based o…
Balancing Summarization and Change Detection in Graph Streams
Shintaro Fukushima, Kenji Yamanishi
This study addresses the issue of balancing graph summarization and graph change detection. Graph summarization compresses large-scale graphs into a smaller scale. However, the que…
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
Xiaohang Xu, Toyotaro Suzumura, Jiawei Yong +5
Next Point-of-Interest (POI) recommendation plays a crucial role in urban mobility applications. Recently, POI recommendation models based on Graph Neural Networks (GNN) have been…
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Renhe Jiang, Zhaonan Wang, Jiawei Yong +6
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity…
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Renhe Jiang, Zhaonan Wang, Jiawei Yong +6
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity…
Detecting Hierarchical Changes in Latent Variable Models
Shintaro Fukushima, Kenji Yamanishi
This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams. There are three different levels of changes for latent variable…
Detecting Change Signs with Differential MDL Change Statistics for COVID-19 Pandemic Analysis
Kenji Yamanishi, Linchuan Xu, Ryo Yuki +2
We are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warni…
Online Robust and Adaptive Learning from Data Streams
Shintaro Fukushima, Atsushi Nitanda, Kenji Yamanishi
In online learning from non-stationary data streams, it is necessary to learn robustly to outliers and to adapt quickly to changes in the underlying data generating mechanism. In t…