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Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings Scientific Reports
Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction The approach of [2] uses inductive logic programming (ILP) to learn model transformation rules. ILP appears to be appropriate for the task of learning tree-to-tree mappings, since trees are naturally representable as Prolog terms. Similar to CGBE, ILP postulates and checks possible 더보기…