Publications (38)
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Tianmeng Yang, Min Zhou, Yujing Wang +4
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many res…
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
Zhe Li, Zhongwen Rao, Lujia Pan +1
Multivariate time series forecasting has been widely used in various practical scenarios. Recently, Transformer-based models have shown significant potential in forecasting tasks d…
CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models
Kairong Han, Wenshuo Zhao, Ziyu Zhao +3
Large Language Models (LLMs) have achieved remarkable success across various domains. However, a fundamental question remains: Can LLMs effectively utilize causal knowledge for pre…
Post-Training in Time Series Foundation Models: A Unifying Framework
Shifeng Xie, Ambroise Odonnat, Zehao Xiao +7
Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deploymen…
Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models
Yunhao Zhang, Ruiying Qi, Jiale Zheng +3
While Next-Token Prediction (NTP) has unified LLM pretraining, its adaptation to unbounded, continuous time series (TS) remains open. To bridge the gap, we introduce UniTok, a univ…
TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction
Min Zhou, Bisheng Li, Menglin Yang +1
Link prediction is a key problem for network-structured data, attracting considerable research efforts owing to its diverse applications. The current link prediction methods focus…