5 citations · 8 across the 13 of their papers we have counts for
5 papers · 1 filter
Does Learning Protein Folding Generalize to Broader Reasoning?
Yong Liu, Zhanpeng Shi, Yizhou Dang +4
Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed…
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…
Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation
Zhifu Wei, Yizhou Dang, Guibing Guo +2
Sequential Recommendation (SR) learns user preferences from their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most items exhibi…
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…