8 papers
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
Peiliang Gong, Emadeldeen Eldele, Chenyu Liu +8
Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment…
Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations
Deyu Zhuang, Peiliang Gong, Yang Shao +4
Accurate Remaining Useful Life prediction is critical for industrial predictive maintenance. However, real-world deployment is challenging due to the irregular nature of sensor obs…
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
Peiliang Gong, Han Zhang, Zhen Jiang +5
Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing…
Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation
Peiliang Gong, Yucheng Wang, Min Wu +3
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained model from an annotated source domain to an unlabelled target domain without accessing the source data, thereby pre…
Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization
Peiliang Gong, Emadeldeen Eldele, Min Wu +3
Foundation models have achieved remarkable success across diverse machine-learning domains through large-scale pretraining on large, diverse datasets. However, pretraining on such…
Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
Peiliang Gong, Mohamed Ragab, Min Wu +4
Test-time adaptation aims to adapt pre-trained deep neural networks using solely online unlabelled test data during inference. Although TTA has shown promise in visual applications…