Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model

Understand

Humans have the ability to rapidly understand rich combinatorial concepts from limited data.

• Here we investigate this ability in the context of auditory signals, which have been evolved in a cultural transmission experiment to study the emergence of combinatorial structure in language.
• We propose a neuro-symbolic generative model which combines the strengths of previous approaches to concept learning.
• Our model performs fast inference drawing on neural network methods, while still retaining the interpretability and generalization from limited data seen in structured generative approaches.