1 citations · 1 across the 12 of their papers we have counts for
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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…
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…
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…
Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang +3
Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consiste…
Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang +5
Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. Ho…