7 citations · 20 across the 34 of their papers we have counts for
14 papers · 1 filter
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…
ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
Chu Zhao, Enneng Yang, Yuting Liu +2
Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels constructed by majority voting. To reduc…
FedLoRA-Optimizer: Federated LoRA Fine-Tuning with Global and Local Optimization in Heterogeneous Data Scenarios
Jianzhe Zhao, Hailin Zhu, Yu Zhang +2
Federated efficient fine-tuning has emerged as an approach that leverages distributed data and computational resources across nodes to address the challenges of large-scale fine-tu…
Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Chu Zhao, Eneng Yang, Yizhou Dang +3
Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward l…
Data Assetization via Resources-decoupled Federated Learning
Jianzhe Zhao, Feida Zhu, Lingyan He +4
With the development of the digital economy, data is increasingly recognized as an essential resource for both work and life. However, due to privacy concerns, data owners tend to…
Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
Chu Zhao, Enneng Yang, Yuliang Liang +3
The distributionally robust optimization (DRO)-based graph neural network methods improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the model's…