A research team from Kumamoto University has developed a promising deep learning model that significantly enhances the accuracy of subgraph matching—a critical task in fields ranging from drug discovery to natural language processing.
Deep learning model dramatically improves subgraph matching accuracy by eliminating noise
Reader’s Picks
-
Myanmar’s history of prolonged conflict has led to the forced displacement and resettlement of generations of refugees to the U.S., [...]
-
During fieldwork in cities in China, I came across a new marital practice, locally described as liang-tou-dun, literally “two places [...]
-
Every year, around 90,000 young people make the transition from school to work. A large number of them start to [...]