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Broken Symmetries in a Location-Invariant Word Recognition Network
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 ruleddoi:10.1162/neco_a_00064 pmid:20964541 fatcat:ih22xaclhrb5tlyin5ae3khyoa