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A key aspect of human intelligence is the ability to infer abstract rulesdirectly from high-dimensional sensory data, and to do so given only a limitedamount of training experience. Deep neural network algorithms have proven to bea powerful tool for learning directly from high-dimensional data, but currentlylack this capacity for data-efficient induction of abstract rules, leading someto argue that symbol-processing mechanisms will be necessary to account forthis capacity. In this work, we take a step toward bridging this gap byintroducing the Emergent Symbol Binding Network (ESBN), a recurrent networkaugmented with an external memory that enables a form of variable-binding andindirection. This binding mechanism allows symbol-like representations toemerge through the learning process without the need to explicitly incorporatesymbol-processing machinery, enabling the ESBN to learn rules in a manner thatis abstracted away from the particular entities to which those rules apply.Across a series of tasks, we show that this architecture displays nearlyperfect generalization of learned rules to novel entities given only a limitednumber of training examples, and outperforms a number of other competitiveneural network architectures.

Emergent Symbols through Binding in External Memory