95 citations · 95 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
TensorFlow-Serving: Flexible, High-Performance ML Serving
Christopher Olston, Noah Fiedel, Kiril Gorovoy +6
We describe TensorFlow-Serving, a system to serve machine learning models inside Google which is also available in the cloud and via open-source. It is extremely flexible in terms…