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• W2953244376 abstract "We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data across modalities. We show that our model indeed learns to predict features of the visual context given phonetically transcribed image descriptions, and show that it represents linguistic information in a hierarchy of levels: lower layers in the stack are comparatively more sensitive to form, whereas higher layers are more sensitive to meaning." @default.
• W2953244376 created "2019-06-27" @default.
• W2953244376 creator A5000074624 @default.
• W2953244376 creator A5022698890 @default.
• W2953244376 date "2016-10-11" @default.
• W2953244376 modified "2023-09-27" @default.
• W2953244376 title "From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning" @default.
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• W2953244376 hasPublicationYear "2016" @default.
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