2 citations · 2 across the 7 of their papers we have counts for
7 papers
Contrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classification
Wenbin Pei, Yunrong Hao, Zhen Liu +4
Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased classifiers on imbalanced da…
Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
Boyuan Li, Zhen Liu, Yicheng Luo +1
Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative fo…
A Unified Shape-Aware Foundation Model for Time Series Classification
Zhen Liu, Yucheng Wang, Boyuan Li +4
Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundatio…
Learning Soft Sparse Shapes for Efficient Time-Series Classification
Zhen Liu, Yicheng Luo, Boyuan Li +3
Shapelets are discriminative subsequences (or shapes) with high interpretability in time series classification. Due to the time-intensive nature of shapelet discovery, existing sha…
HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting
Boyuan Li, Yicheng Luo, Zhen Liu +3
Irregular multivariate time series (IMTS) are characterized by irregular time intervals within variables and unaligned observations across variables, posing challenges in learning…
Incremental Sequence Labeling: A Tale of Two Shifts
Shengjie Qiu, Junhao Zheng, Zhen Liu +2
The incremental sequence labeling task involves continuously learning new classes over time while retaining knowledge of the previous ones. Our investigation identifies two signifi…
Beyond Anti-Forgetting: Multimodal Continual Instruction Tuning with Positive Forward Transfer
Junhao Zheng, Qianli Ma, Zhen Liu +2
Multimodal Continual Instruction Tuning (MCIT) enables Multimodal Large Language Models (MLLMs) to meet continuously emerging requirements without expensive retraining. MCIT faces…