Neural Comput - Broken symmetries in a location-invariant word recognition network.

Tópicos

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Resumo

We studied the feedforward network proposed by Dandurand et al. (2010), which maps location-specific letter inputs to location-invariant word outputs, probing the hidden layer to determine the nature of the code. Hidden patterns for words were densely distributed, and K-means clustering on single letter patterns produced evidence that the network had formed semi-location-invariant letter representations during training. The possible confound with superseding bigram representations was ruled out, and linear regressions showed that any word pattern was well approximated by a linear combination of its constituent letter patterns. Emulating this code using overlapping holographic representations (Plate, 1995) uncovered a surprisingly acute and useful correspondence with the network, stemming from a broken symmetry in the connection weight matrix and related to the group-invariance theorem (Minsky & Papert, 1969). These results also explain how the network can reproduce relative and transposition priming effects found in humans.

Resumo Limpo

studi feedforward network propos dandurand et al map locationspecif letter input locationinvari word output probe hidden layer determin natur code hidden pattern word dens distribut kmean cluster singl letter pattern produc evid network form semilocationinvari letter represent train possibl confound supersed bigram represent rule linear regress show word pattern well approxim linear combin constitu letter pattern emul code use overlap holograph represent plate uncov surpris acut use correspond network stem broken symmetri connect weight matrix relat groupinvari theorem minski papert result also explain network can reproduc relat transposit prime effect found human

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