5 papers
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.…
Evaluating Memory Capability in Continuous Lifelog Scenario
Jianjie Zheng, Zhichen Liu, Zhanyu Shen +6
Nowadays, wearable devices can continuously lifelog ambient conversations, creating substantial opportunities for memory systems. However, existing benchmarks primarily focus on on…
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…