The experiment was carried out in two stages. In the first one, different activation functions, GLN, Tanh, and Sine, were tested in an MLP-type Autoencoder neural network model. Different compression ...

Abstract: Neural networks (NN) are architectures and algorithms ... In this paper, we propose a denoising autoencoder approach using a modified Elliott activation function and a cost function that ...

If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning models are developed.

An autoencoder is a type of artificial neural network commonly used to learn efficient representations ... It consists of two main parts: the encoder and the decoder. The function of the encoder is to ...

This is a simple example of using a neural network as an autoencoder without using any machine learning ... The feedforward and backpropagation algorithm equations, using the sigmoid function as an ...

Performance on the validation set after optimizing the weight of loss function. Although the ... fully connected neural network; MCC, Matthews correlation coefficient; SAE, stacked autoencoder; SE, ...