3 citations · 6 across the 19 of their papers we have counts for
8 papers · 1 filter
Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
Li Sun, Zhenhao Huang, Yiding Wang +3
Foundation models have sparked a revolution via a pretraining-adaptation paradigm, with recent efforts extending this success to graphs. Unlike other modalities, graphs contain ric…
ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
Chengze Li, Lingwei Wei, Li Sun +7
Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a single reusable solver. On…
A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
Kaiwen Luo, Zhenhong Zhou, Leyan Wang +32
Advances in Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs). Among these, Large Audio Language Models (LALMs) are essential for realizi…
Riemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligence
Philip S. Yu, Li Sun
Graphs provide a natural description of the complex relationships among objects, and play a pivotal role in communications, transportation, social computing, the life sciences, etc…
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
Li Sun, Zhenhao Huang, Silei Chen +4
Multi-domain graph pre-training integrates knowledge from diverse domains to enhance performance in the target domains, which is crucial for building graph foundation models. Despi…
Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
Li Sun, Lanxu Yang, Jiayu Tian +6
Detecting out-of-distribution (OOD) graphs is crucial for ensuring the safety and reliability of Graph Neural Networks. In unsupervised graph-level OOD detection, models are typica…
Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger
Li Sun, Ming Zhang, Wenxin Jin +5
Hypergraphs are the natural description of higher-order interactions among objects, widely applied in social network analysis, cross-modal retrieval, etc. Hypergraph Neural Network…
RiemannGL: Riemannian Geometry Changes Graph Deep Learning
Li Sun, Qiqi Wan, Suyang Zhou +2
Graphs are ubiquitous, and learning on graphs has become a cornerstone in artificial intelligence and data mining communities. Unlike pixel grids in images or sequential structures…