4 papers
LLM-Generated Explanations Do Not Suffice for Ultra-Strong Machine Learning
Lun Ai, Johannes Langer, Ute Schmid +1
Ultra Strong Machine Learning (USML) refers to symbolic learning systems that not only improve their own performance but can also teach their acquired knowledge to quantifiably imp…
Boolean Matrix Logic Programming on the GPU
Lun Ai
Traditional logic programming relies on symbolic computation on the CPU, which can limit performance for large-scale inference tasks. Recent advances in GPU hardware enable high-th…
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models
Lun Ai, Stephen H. Muggleton, Shi-Shun Liang +1
Reasoning about hypotheses and updating knowledge through empirical observations are central to scientific discovery. In this work, we applied logic-based machine learning methods…
Active learning of digenic functions with boolean matrix logic programming
Lun Ai, Stephen H. Muggleton, Shi-shun Liang +1
We apply logic-based machine learning techniques to facilitate cellular engineering and drive biological discovery, based on comprehensive databases of metabolic processes called g…