6 citations · 12 across the 8 of their papers we have counts for
6 papers · 1 filter
Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategy
Lun Ai, Johannes Langer, Ute Schmid +1
Active learning is a general learning mechanism shared by artificial and human learners. Whether AI can teach humans such a strategy that transfers across domains is an open questi…
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…
Simulating Petri nets with Boolean Matrix Logic Programming
Lun Ai, Stephen H. Muggleton, Shi-Shun Liang +1
Recent attention to relational knowledge bases has sparked a demand for understanding how relations change between entities. Petri nets can represent knowledge structure and dynami…
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks
Lun Ai, Shi-Shun Liang, Wang-Zhou Dai +3
An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to hug…
Explanatory machine learning for sequential human teaching
Lun Ai, Johannes Langer, Stephen H. Muggleton +1
The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theorie…
Beneficial and Harmful Explanatory Machine Learning
Lun Ai, Stephen H. Muggleton, Céline Hocquette +2
Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this con…