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The recent successful deep neural networks are largely trained in a supervised manner. It associates complex patterns of input samples with neurons in the last layer, which form representations of concepts. In spite of their successes, the properties of complex patterns associated a learned concept remain elusive. In this work, by analyzing how neurons are associated with concepts in supervised networks, we hypothesize that with proper priors to regulate learning, neural networks canarXiv:1612.09438v2 fatcat:eogrgrxogfa4nlgyreu422pa7u