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- W2140622310 abstract "One approach to invariant object recognition employs a recurrent neural network as an associative memory. In the standard depiction of the network's state space, memories of objects are stored as attractive fixed points of the dynamics. I argue for a modification of this picture: if an object has a continuous family of instantiations, it should be represented by a continuous attractor. This idea is illustrated with a network that learns to complete patterns. To perform the task of filling in missing information, the network develops a continuous attractor that models the manifold from which the patterns are drawn. From a statistical view-point, the pattern completion task allows a formulation of unsupervised learning in terms of regression rather than density estimation." @default.
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- W2140622310 title "Learning Continuous Attractors in Recurrent Networks" @default.
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