Softened Symbol Grounding for Neuro-symbolic Systems

1 code implementation • 1 Mar 2024 • Zenan Li, Yuan YAO, Taolue Chen, Jingwei Xu, Chun Cao, Xiaoxing Ma, Jian Lü

Neuro-symbolic learning generally consists of two separated worlds, i. e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI.

Operational Calibration: Debugging Confidence Errors for DNNs in the Field

no code implementations • 6 Oct 2019 • Zenan Li, Xiaoxing Ma, Chang Xu, Jingwei Xu, Chun Cao, Jian Lü

Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field.

Boosting Operational DNN Testing Efficiency through Conditioning

1 code implementation • 6 Jun 2019 • Zenan Li, Xiaoxing Ma, Chang Xu, Chun Cao, Jingwei Xu, Jian Lü

With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditions.

DNN Testing