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 ...

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 ...

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 ...

In this paper we present a novel approach to interpretable AI inspired by Quantum Field Theory (QFT) which we call the NCoder. The NCoder is a modified autoencoder ...

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, ...

Neural networks accept only numeric data and so the ... instead of the 9-6-9 architecture of the demo autoencoder. A neural autoencoder is essentially a complex mathematical function that predicts its ...

that a neural network with a single hidden layer and enough hidden nodes can approximate any function that can be approximated by a deep autoencoder.