New adaptable and trainable activation functions have been studied lately, which are used to increase network performance. This study intends to evaluate the performance of an artificial neuron that ...

In this paper, we propose a denoising autoencoder approach using a modified Elliott activation function and a cost function that favors sparsity in the input data. Preliminary experiments using the ...

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.

Many of the autoencoder examples I see online use relu() activation for interior layers. The relu() function was designed for use with very deep neural architectures. For autoencoders, which are ...

This repository presents a novel autoencoder algorithm designed using the Hartley Transform as a key component, coupled with involutional activation functions. The architecture incorporates various ...

The script helps to train your own Deep Autoencoder with Extreme Learning Machines. It performs a Deep Autoencoder model with with a specified model. After that, it utilizes both Neural Networks and ...

Behind the scenes, the autoencoder uses tanh() activation on the hidden nodes and tanh() activation on the output nodes. The result of the tanh() function is always between -1 and +1. Therefore, the ...