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task, named correlation encoder-decoder model (CED). By maximizing the mutual information, CED learns ideal text representations and reduces information loss in the encoder-decoder model. As far as we know, this is the first work for leveraging MI to achieve end-to- end text generation.
We propose a novel correlation encoder-decoder model, which optimizes both the encoder and the decoder to reduce the problem of information loss.
A novel correlation encoder-decoder model is proposed, which optimizes both the encoder and the decoder to reduce the problem of information loss by ...
It also incorporates and streamlines the expertise of radiologists and doctors for detecting and categorizing the complicated patterns of diseases within ...
Mar 19, 2024 · Greedy decoding is not generally a good way of producing text from a LM(but is a viable strategy when the output is.
The seq2seq (sequence to sequence) model is a type of encoder-decoder deep learning model commonly employed in natural language processing.
Missing: Correlation | Show results with:Correlation
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May 25, 2023 · Encoder-Decoder models are the base models of google-translate once but now, they are using BERT and TRANSFORMER based models.
Deep Neural Networks (DNNs) are powerful models which have already achieved good performance for different machine learning tasks like speech recognition [1], ...
Dec 17, 2023 · Decoder models are limited to the product of auto-regressive task while encoder models give contextual representations that can be fine-tuned on other decoder ...
Missing: Correlation | Show results with:Correlation
Oct 11, 2024 · The results showed that encoder-decoder models generally outperformed their decoder-only counterparts in translation quality and contextual understanding.