Topic: semantic embeddings

Fast and adaptable learning is often characterized by sudden jumps in performance rather than slow adaptation typical of updating synaptic weights. Cognitive science theories posit that rapid insights are due to compositional symbolic representations that allow animals to quickly recombine past knowledge to adapt to new settings. Traditionally, it has been difficult to model this type of compositional learning with neural systems. Here we leverage recent work on compositional computations in multitasking Recurrent Neural Networks (RNNs) to construct a neural model of compositional learning. Pa