📝 Publications

OccamVTS: Distilling Vision Models to 1% Parameters for Time Series Forecasting
Sisuo Lyu, Siru Zhong, Weilin Ruan, Qingxiang Liu, Qingsong Wen, Hui Xiong, Yuxuan Liang
[Paper]

• This work proposes OccamVTS, a novel knowledge distillation framework that extracts only the essential 1% of predictive information from large vision models (LVMs) to address the severe parameter redundancy and semantic misalignment challenges in time series forecasting by employing pre-trained LVMs as privileged teachers to guide lightweight student networks through pyramid-style feature alignment and selective distillation, effectively filtering out irrelevant high-level semantic noise while preserving crucial low-level temporal patterns.

Multi-view Hypergraph-based Contrastive Learning Model for Cold-Start Micro-video Recommendation (Oral)
Sisuo Lyu, Xiuze Zhou, Xuming Hu
[Paper]  [Code]  [PPT]  [Poster]

• This work proposes MHCR, a novel framework that leverages multi-view hypergraph structures and contrastive learning to address the challenges of over-smoothing and cold-start problems in micro-video recommendation systems by effectively utilizing multimodal features and self-supervised learning signals.