A more recent neural network architecture for text simplification is the copy-attention model, which extends the encoder-decoder model with a copy mechanism and an attention mechanism.

Connection between RNN and Encoder-Decoder: Sequential Processing: Both the encoder and decoder in the Encoder-Decoder architecture are typically implemented using RNNs (or it's variants like LSTM or ...

Neural networks (NNs) and graph signal processing have emerged as important actors in data-science applications dealing with complex (non-linear, non-Euclidean) datasets. In this work, we introduce a ...

Autoencoders enable us to distil information by utilising a neural network architecture composed of an encoder and decoder. There are multiple types of autoencoders that vary based on their structure ...

Encoder and Decoder Architecture. In 2015, Sequence to Sequence Learning with Neural Network became a very popular architecture and with that the encoder-decoder architecture also became part of wide ...

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), ...