Fig. 2
From: Revealing ferroelectric switching character using deep recurrent neural networks
Drawing of sparse long–short-term memory autoencoder. Diagram shows the three components of the neural network the encoder, embedding layer, and decoder. Within each of these sections the dimensionality and size of the input and outputs are indicated on the right. Diagram shows how temporal data (represented as linear color-changing arrows) is consider through the inclusion of recurrent long–short-term memory neurons. Solid colored arrows indicate just a single time step is passed, where arrows with gradients imply the passing of temporally dependent vectors. In the figure l is the number of encoding layers, m is the number of decoding layers, Nenc is the number of neurons in the encoding layer, Nemb is the number of neurons in the embedding layer, and Ndec is the number of neurons in the decoding layer
