A bidirectional LSTM (BiLSTM) layer learns bidirectional long-term dependencies between time steps of time series or sequence data. These dependencies can be useful when you want the network to learn from the complete time series at each time step.
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In this paper, we propose a stacked Bidirectional Long Short-Term Memory (BiLSTM) neural network based on the coattention mechanism to extract the interaction between questions and answers, combining cosine similarity and Euclidean distance to score the question and answer sentences.
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Hi everyone, the above graph is produced by a BiLSTM model i just trained and tested. I can't seem to interpret it while it is very different from the references that i acquired by googling online. ...
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Hannun et al. used an original ECG time series as input with a 34-layer CNN and generated a prediction tag once per second; ... The experimental results show that the MLCNN-BiLSTM proposed in this paper has the best performance among several models, its ACC is 87.81%, and it is effective in dealing with ECG classification.
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(Although the author said in the paper that two SD self-networks, BiLSTM-SD and FS-SD, are proposed, the author only introduces the BiLSTM-SD subnet. The following uses it as an example to explain the principle and process) Step 1: Use the output h obtained in the previous step and use the formula
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Dec 10, 2017 · An introduction to long short term memory. In this article learn about long short term memory network and architecture of lstm in deep learning.