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Title:
Towards a theory of encode/decoder architectures
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Abstract:
A common choice of architecture in representation learning (i.e. learning a good "embedding" of the data) is an "encoder/decoder" architecture -- which tries to map a part of the input into a good latent representation (via an "encoder"), and predict the remaining part of the input (via a "decoder"). Two common examples are universal machine translation: where one tries to learn to translate between any pair of a set of languages via a common "latent language", given paired up corpora for only a part of the pairs; and contextual encoders -- where one tries to predict a part of the image, given the rest of the image.
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