To make sequence-to-sequence predictions using a LSTM, we use an encoder-decoder architecture. The LSTM encoder-decoder consists of two LSTMs. The first LSTM, or the encoder, processes an input ...

Some of the applications are: Before going deeper into the network, we should have some prior knowledge about the RNN and LSTM models. This can be obtained by using this article. Now, let’s have a ...

This work is a loose implementation of the work described in this paper: Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder The main idea is to build an LSTM ...

This Seq2Seq modelling is performed by the LSTM encoder and decoder. We can guess this process from the below illustration. (Image Source: blog.keras.io) In this article, we will implement deep ...

Previously encoder-decoder models were ... Hence we use LSTM to encode each word into a vector, then pass these vectors into the attention layer and pass the output to another decoder model to get the ...

This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder–decoder network to segment out ...

In this paper we present a Long Short-Term Memory (LSTM) encoder-decoder classifier for scientific abstracts. As a large corpus of annotated abstracts was required to train our classifier, we built a ...