3 papers
cs.LG2026
Structural Compositional Function Networks: Interpretable Functional Compositions for Tabular Discovery
Fang Li
Despite the ubiquity of tabular data in high-stakes domains, traditional deep learning architectures often struggle to match the performance of gradient-boosted decision trees whil…
eess.IV2025
Semantic Temporal Single-photon LiDAR
Fang Li, Tonglin Mu, Shuling Li +11
Temporal single-photon (TSP-) LiDAR presents a promising solution for imaging-free target recognition over long distances with reduced size, cost, and power consumption. However, e…
cs.LG2025
Compositional Function Networks: A High-Performance Alternative to Deep Neural Networks with Built-in Interpretability
Fang Li
Deep Neural Networks (DNNs) deliver impressive performance but their black-box nature limits deployment in high-stakes domains requiring transparency. We introduce Compositional Fu…