Abstract: In this article, a stochastic recurrent encoder decoder neural network (SREDNN), which considers latent random variables in its recurrent structures, is developed for the first time for the ...

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 ...

Transformers are very powerful, and also very complex. They use a dense feedforward network as a sub-neural net inside the encoder and decoder components. They also demand considerable computing ...

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 ...

Zhu RC, Wang JF, Qiu TS, Yang DK, Feng B et al. Direct field-to-pattern monolithic design of holographic metasurface via residual encoder-decoder convolutional neural network. Opto-Electron Adv 6 ...

In this article, a stochastic recurrent encoder decoder neural network (SREDNN), which considers latent random variables in its recurrent structures, is developed for the first time for the generative ...