20 citations · 33 across the 5 of their papers we have counts for
5 papers
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…
A Co-training Approach for Noisy Time Series Learning
Weiqi Zhang, Jianfeng Zhang, Jia Li +1
In this work, we focus on robust time series representation learning. Our assumption is that real-world time series is noisy and complementary information from different views of t…
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning
Zhiyu Liang, Jianfeng Zhang, Chen Liang +3
Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable represe…
Inducing Neural Collapse in Deep Long-tailed Learning
Xuantong Liu, Jianfeng Zhang, Tianyang Hu +3
Although deep neural networks achieve tremendous success on various classification tasks, the generalization ability drops sheer when training datasets exhibit long-tailed distribu…
Predicting Path Failure In Time-Evolving Graphs
Jia Li, Zhichao Han, Hong Cheng +4
In this paper we use a time-evolving graph which consists of a sequence of graph snapshots over time to model many real-world networks. We study the path classification problem in…