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…
Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning
Xudong Wang, Guoming Tang, Junyu Xue +3
Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and building applications. However, the…
Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
Xudong Wang, Ziheng Sun, Chris Ding +1
This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effectively capturing both structural p…
CLUBench: A Clustering Benchmark
Feng Xiao, Dazhi Fu, Chris Ding +1
Clustering is a fundamental problem in data science with a long-standing research history, yielding numerous insightful algorithms. Despite this progress, a systematic and large-sc…
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…