5 papers
Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models
Jianqi Zhang, Xingyu Zhang, Zeen Song +3
Time series forecasting (TSF) plays an important role in a wide range of real-world applications. Recently, time series foundation models (TSFMs), pretrained on large-scale dataset…
Multi-modal Test-time Adaptation via Adaptive Probabilistic Gaussian Calibration
Jinglin Xu, Yi Li, Chuxiong Sun +3
Multi-modal test-time adaptation (TTA) enhances the resilience of benchmark multi-modal models against distribution shifts by leveraging the unlabeled target data during inference.…
Enhancing Large Language Models for Time-Series Forecasting via Vector-Injected In-Context Learning
Jianqi Zhang, Jingyao Wang, Wenwen Qiang +2
The World Wide Web needs reliable predictive capabilities to respond to changes in user behavior and usage patterns. Time series forecasting (TSF) is a key means to achieve this go…
AmPLe: Supporting Vision-Language Models via Adaptive-Debiased Ensemble Multi-Prompt Learning
Fei Song, Yi Li, Jiangmeng Li +4
Multi-prompt learning methods have emerged as an effective approach for facilitating the rapid adaptation of vision-language models to downstream tasks with limited resources. Exis…
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning
Bin Qin, Qirui Ji, Jiangmeng Li +4
Self-supervised topological deep learning (TDL) represents a nascent but underexplored area with significant potential for modeling higher-order interactions in simplicial complexe…