7 papers
Mapping and Measuring the Behavioral Evolution of Large Language Models
Dong Qiao, Chris Ding, Jicong Fan
Benchmark leaderboards summarize how well a language model performs, but not how its behavior relates to that of other models or changes across generations. We characterize the out…
GraphProp: Training the Graph Foundation Models using Graph Properties
Ziheng Sun, Qi Feng, Lehao Lin +2
This work focuses on training graph foundation models (GFMs) that have strong generalization ability in graph-level tasks such as graph classification. Effective GFM training requi…
Adaptive Riemannian Graph Neural Networks
Xudong Wang, Chris Ding, Tongxin Li +1
Graph data often exhibits complex geometric heterogeneity, where structures with varying local curvature, such as tree-like hierarchies and dense communities, coexist within a sing…
Explainable Graph Representation Learning via Graph Pattern Analysis
Xudong Wang, Ziheng Sun, Chris Ding +1
Explainable artificial intelligence (XAI) is an important area in the AI community, and interpretability is crucial for building robust and trustworthy AI models. While previous wo…
Mutual Regression Distance
Dong Qiao, Jicong Fan
The maximum mean discrepancy and Wasserstein distance are popular distance measures between distributions and play important roles in many machine learning problems such as metric…
Federated t-SNE and UMAP for Distributed Data Visualization
Dong Qiao, Xinxian Ma, Jicong Fan
High-dimensional data visualization is crucial in the big data era and these techniques such as t-SNE and UMAP have been widely used in science and engineering. Big data, however,…