4 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Negatives-Dominant Contrastive Learning for Generalization in Imbalanced Domains
Meng Cao, Jiexi Liu, Songcan Chen
Imbalanced Domain Generalization (IDG) focuses on mitigating both domain and label shifts, both of which fundamentally shape the model's decision boundaries, particularly under het…
Beyond Observations: Reconstruction Error-Guided Irregularly Sampled Time Series Representation Learning
Jiexi Liu, Meng Cao, Songcan Chen
Irregularly sampled time series (ISTS), characterized by non-uniform time intervals with natural missingness, are prevalent in real-world applications. Existing approaches for ISTS…
TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis
Jiexi Liu, Meng Cao, Songcan Chen
Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Due to their non-uniform intervals between successive observations and varying sampling rates among s…
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis
Jiexi Liu, Meng Cao, Songcan Chen
Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Most existing methods treat ISMTS as synchronized regularly sampled time series with missing values,…
A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends
Xiang Li, Jiexi Liu, Xinrui Wang +1
In reality, data often exhibit associations with multiple labels, making multi-label learning (MLL) become a prominent research topic. The last two decades have witnessed the succe…
TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning
Jiexi Liu, Songcan Chen
Learning universal time series representations applicable to various types of downstream tasks is challenging but valuable in real applications. Recently, researchers have attempte…