1 citations · 1 across the 3 of their papers we have counts for
5 papers
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting
Xinxing Zhou, Jiaqi Ye, Shubao Zhao +6
In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning. Traditional foreca…
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting
Shubao Zhao, Ming Jin, Zhaoxiang Hou +4
Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained si…
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
Zengxiang Li, Zhaoxiang Hou, Hui Liu +8
Multimodal data, which can comprehensively perceive and recognize the physical world, has become an essential path towards general artificial intelligence. However, multimodal larg…
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Yulan Gao, Zhaoxiang Hou, Chengyi Yang +2
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental ch…
FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning
Leiming Chen, Weishan Zhang, Cihao Dong +5
Traditional federated learning uses the number of samples to calculate the weights of each client model and uses this fixed weight value to fusion the global model. However, in pra…