6 citations · 10 across the 13 of their papers we have counts for
13 papers
Causal Time Series Generation via Diffusion Models
Yutong Xia, Chang Xu, Yuxuan Liang +4
Time series generation (TSG) synthesizes realistic sequences and has achieved remarkable success. Among TSG, conditional models generate sequences given observed covariates, howeve…
CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series
Yutong Xia, Yingying Zhang, Yuxuan Liang +3
Time series anomaly detection has garnered considerable attention across diverse domains. While existing methods often fail to capture the underlying mechanisms behind anomaly gene…
JointDistill: Adaptive Multi-Task Distillation for Joint Depth Estimation and Scene Segmentation
Tiancong Cheng, Ying Zhang, Yuxuan Liang +3
Depth estimation and scene segmentation are two important tasks in intelligent transportation systems. A joint modeling of these two tasks will reduce the requirement for both the…
Reimagining Urban Science: Scaling Causal Inference with Large Language Models
Yutong Xia, Ao Qu, Yunhan Zheng +8
Urban causal research is essential for understanding the complex, dynamic processes that shape cities and for informing evidence-based policies. However, current practices are ofte…
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Xu Liu, Juncheng Liu, Gerald Woo +7
Time series foundation models have demonstrated impressive performance as zero-shot forecasters. However, achieving effectively unified training on time series remains an open chal…
Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach
Huaiwu Zhang, Yutong Xia, Siru Zhong +5
The increasing number of vehicles highlights the need for efficient parking space management. Predicting real-time Parking Availability (PA) can help mitigate traffic congestion an…
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
Kun Wang, Hao Wu, Guibin Zhang +5
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.…
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
Yutong Xia, Runpeng Yu, Yuxuan Liang +3
Graph Neural Networks have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of a…
UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web
Yibo Yan, Haomin Wen, Siru Zhong +5
Urban region profiling from web-sourced data is of utmost importance for urban planning and sustainable development. We are witnessing a rising trend of LLMs for various fields, es…
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Junfeng Hu, Xu Liu, Zhencheng Fan +2
Spatio-temporal graph learning is a fundamental problem in modern urban systems. Existing approaches tackle different tasks independently, tailoring their models to unique task cha…
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
Xu Liu, Junfeng Hu, Yuan Li +4
Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific…
Domain-wise Invariant Learning for Panoptic Scene Graph Generation
Li Li, You Qin, Wei Ji +2
Panoptic Scene Graph Generation (PSG) involves the detection of objects and the prediction of their corresponding relationships (predicates). However, the presence of biased predic…
Towards Complex-query Referring Image Segmentation: A Novel Benchmark
Wei Ji, Li Li, Hao Fei +4
Referring Image Understanding (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms. However, there has been a lack of research…