20 citations · 20 across the 12 of their papers we have counts for
13 papers
Efficient Document Parsing via Parallel Token Prediction
Lei Li, Ze Zhao, Meng Li +6
Document parsing, as a fundamental yet crucial vision task, is being revolutionized by vision-language models (VLMs). However, the autoregressive (AR) decoding inherent to VLMs cre…
Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
Dongyuan Li, Shun Zheng, Chang Xu +2
Time series forecasting has attracted significant attention in the field of AI. Previous works have revealed that the Channel-Independent (CI) strategy improves forecasting perform…
ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution
Hui Sun, Chang Xu, Haonan Xie +7
LLM-driven Anomaly Detection (AD) helps enhance the understanding and explanatory abilities of anomalous behaviors in Time Series (TS). Existing methods face challenges of inadequa…
OATS: Online Data Augmentation for Time Series Foundation Models
Junwei Deng, Chang Xu, Jiaqi W. Ma +5
Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. E…
Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
Xu Zhang, Junwei Deng, Chang Xu +2
Time series generation (TSG) is widely used across domains, yet most existing methods assume regular sampling and fixed output resolutions. These assumptions are often violated in…
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…
MIRA: Medical Time Series Foundation Model for Real-World Health Data
Hao Li, Bowen Deng, Chang Xu +8
A unified foundation model for medical time series -- pretrained on open access and ethics board-approved medical corpora -- offers the potential to reduce annotation burdens, mini…
TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation
Bowen Deng, Chang Xu, Hao Li +3
Synthetic Electronic Health Record (EHR) time-series generation is crucial for advancing clinical machine learning models, as it helps address data scarcity by providing more train…
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling
Hao Li, Yu-Hao Huang, Chang Xu +5
Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis. While existing methods have shown…
TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts
Yu-Hao Huang, Chang Xu, Yueying Wu +2
Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to ge…
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting
Huanyu Zhang, Chang Xu, Yi-Fan Zhang +4
Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation model…
InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models
Min Hou, Yueying Wu, Chang Xu +4
As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These models learn the underlying high-…
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
Yiyuan Yang, Ming Jin, Haomin Wen +9
Diffusion models have been widely used in time series and spatio-temporal data, enhancing generative, inferential, and downstream capabilities. These models are applied across dive…