3 citations · 4 across the 12 of their papers we have counts for
6 papers · 1 filter
AsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting
Zhixiang Wu, Yining Liu, Bo Zhao +4
Multi-modal spatio-temporal forecasting (MM-STF) supports weather nowcasting, traffic prediction, and earth-system modeling by combining heterogeneous sources such as physical fiel…
Distilling Time Series Foundation Models for Efficient Forecasting
Yuqi Li, Kuiye Ding, Chuanguang Yang +2
Time Series foundation models (TSFMs) deliver strong forecasting performance through large-scale pretraining, but their large parameter sizes make deployment costly. While knowledg…
DDTime: Dataset Distillation with Spectral Alignment and Information Bottleneck for Time-Series Forecasting
Yuqi Li, Kuiye Ding, Chuanguang Yang +5
Time-series forecasting is fundamental across many domains, yet training accurate models often requires large-scale datasets and substantial computational resources. Dataset distil…
Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting
Yuqi Li, Chuanguang Yang, Hansheng Zeng +5
Spatiotemporal forecasting tasks, such as traffic flow, combustion dynamics, and weather forecasting, often require complex models that suffer from low training efficiency and high…
Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection
Yuqi Li, Xingyou Lin, Yanli Li +6
As a special type of multimedia data, Lithography Hotspot Detection (LHD) training often requires stronger privacy protection than conventional multimedia data, and federated learn…
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models
Yuqi Li, Yao Lu, Junhao Dong +7
Layer pruning has emerged as a potent approach to remove redundant layers in the pre-trained network on the purpose of reducing network size and improve computational efficiency. H…