3 citations · 3 across the 3 of their papers we have counts for
4 papers
RUQuant: Towards Refining Uniform Quantization for Large Language Models
Han Liu, Haotian Gao, Changya Li +4
The increasing size and complexity of large language models (LLMs) have raised significant challenges in deployment efficiency, particularly under resource constraints. Post-traini…
How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning
Haotian Gao, Zheng Dong, Jiawei Yong +3
Spatio-temporal forecasting is essential for real-world applications such as traffic management and urban computing. Although recent methods have shown improved accuracy, they ofte…
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Zheng Dong, Renhe Jiang, Haotian Gao +4
Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and lever…
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
Haotian Gao, Renhe Jiang, Zheng Dong +3
Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather. Accurate prediction of spatiotemporal series remains challeng…