1 citations · 2 across the 5 of their papers we have counts for
5 papers
S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF
Wei Chen, Guanghui Zhu, Yafei Li +2
Reinforcement learning from human feedback (RLHF) with preference-based reward models often exhibits unstable training dynamics. A key contributing factor is that standard RLHF rel…
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
Guanghui Zhu, Zipeng Ji, Jingyan Chen +3
GNAS (Graph Neural Architecture Search) has demonstrated great effectiveness in automatically designing the optimal graph neural architectures for multiple downstream tasks, such a…
GEFM: Graph-Enhanced EEG Foundation Model
Limin Wang, Toyotaro Suzumura, Hiroki Kanezashi
Electroencephalography (EEG) signals provide critical insights for applications in disease diagnosis and healthcare. However, the scarcity of labeled EEG data poses a significant c…
On Data Imbalance in Molecular Property Prediction with Pre-training
Limin Wang, Masatoshi Hanai, Toyotaro Suzumura +2
Revealing and analyzing the various properties of materials is an essential and critical issue in the development of materials, including batteries, semiconductors, catalysts, and…
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?
Shun Takashige, Masatoshi Hanai, Toyotaro Suzumura +2
The prediction of material properties plays a crucial role in the development and discovery of materials in diverse applications, such as batteries, semiconductors, catalysts, and…