14 papers
Enhancing Protein Representation Learning via Manifold Restore Mixing
Yizhou Dang, Chuang Zhao, Lianbo Ma +3
Data augmentation (DA) has been proven to be an effective means for improving protein representation learning (PRL) by generating additional training samples. Although mainstream p…
Pay Attention to Sequence Split: Uncovering the Impacts of Sub-Sequence Splitting on Sequential Recommendation Models
Yizhou Dang, Yifan Wu, Minhan Huang +5
Sub-sequence splitting (SSS) has been demonstrated as an effective approach to mitigate data sparsity in sequential recommendation (SR) by splitting a raw user interaction sequence…
Tail-Aware Data Augmentation for Long-Tail Sequential Recommendation
Yizhou Dang, Zhifu Wei, Minhan Huang +4
Sequential recommendation (SR) learns user preferences based on their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most users ca…
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
Enneng Yang, Li Shen, Guibing Guo +4
Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive compu…
MoFu: Scale-Aware Modulation and Fourier Fusion for Multi-Subject Video Generation
Run Ling, Ke Cao, Jian Lu +15
Multi-subject video generation aims to synthesize videos from textual prompts and multiple reference images, ensuring that each subject preserves natural scale and visual fidelity.…
RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation
Run Ling, Wenji Wang, Yuting Liu +12
Personalized image generation is crucial for improving the user experience, as it renders reference images into preferred ones according to user visual preferences. Although effect…