Attention is a mechanism combined in the RNN allowing it to focus on certain parts of the input sequence when predicting a certain part of the output sequence, enabling easier learning and of higher quality.
Jan 30, 2021 · Attention mechanism helps to look at all hidden states from encoder sequence for making predictions unlike vanilla Encoder-Decoder approach.
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Mar 10, 2022 · In this article, we discuss the motivation behind developing an attention mechanism, which helps predictive models to focus on certain parts ...
Dec 10, 2022 · Attention is a mechanism in deep learning that determines the strength of relationship between pieces of data. This is commonly used in ...
Aug 7, 2019 · Attention is a mechanism that was developed to improve the performance of the Encoder-Decoder RNN on machine translation.
Duration: 22:19
Posted: May 4, 2021
Posted: May 4, 2021
Sep 8, 2016 · The attention distribution is usually generated with content-based attention. The attending RNN generates a query describing what it wants to ...
Nov 28, 2023 · An attention mechanism is an Encoder-Decoder kind of neural network architecture that allows the model to focus on specific sections of the ...
May 22, 2020 · The main idea behind attention technique is that it allows the decoder to "look back” at the complete input and extracts significant information ...
Nov 20, 2019 · Attention mechanisms is a layer of neural networks added to deep learning models to focus their attention to specific parts of data, based on ...