4 papers · 1 filter
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Ruichen Xu, Jingxiang Qu, Wenhan Gao +5
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit neg…
Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation
Fang Wan, Jingxiang Qu, Yi Liu
Bayesian inference provides a principled framework for modeling epistemic uncertainty in neural networks by treating predictions as distributions rather than deterministic values.…
GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation
Jingxiang Qu, Wenhan Gao, Ruichen Xu +1
Gaussian Probability Path based Generative Models (GPPGMs) generate data by reversing a stochastic process that progressively corrupts samples with Gaussian noise. Despite state-of…
RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation
Jingxiang Qu, Wenhan Gao, Jiaxing Zhang +4
3D Geometric Graph Neural Networks (GNNs) have emerged as transformative tools for modeling molecular data. Despite their predictive power, these models often suffer from limited i…