Papers with re-constructive and associative nature of human memory

1 papers
Associative Multichannel Autoencoder for Multimodal Word Representation (D18-1)

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Challenge: Existing models that represent word meanings from word co-occurrences ignore associations between modalities and lack ability to transfer information between .
Approach: They propose a novel associative multichannel autoencoder that integrates textual, visual and auditory inputs to learn multimodal word representations.
Outcome: The proposed model outperforms strong unimodal models and state-of-the-art models on six benchmark concepts similarity tests.

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