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Analogical mapping and inference with binary spatter codes and sparse distributed memory
2013
The 2013 International Joint Conference on Neural Networks (IJCNN)
Analogy-making is a key function of human cognition. Therefore, the development of computational models of analogy that automatically learn from examples can lead to significant advances in cognitive systems. Analogies require complex, relational representations of learned structures, which is challenging for both symbolic and neurally inspired models. Vector symbolic architectures (VSAs) are a class of connectionist models for the representation and manipulation of compositional structures,
doi:10.1109/ijcnn.2013.6706829
dblp:conf/ijcnn/EmruliGS13
fatcat:bm34lczbhbaebobtu2vlz5bxyq