Activation functions are essential keys to good performance in a neural network. Many functions can be used, and the choice of which one to use depends on the issues addressed. New adaptable and ...

Neural networks (NN) are architectures and algorithms for machine learning. They are quite powerful for tasks like classification, clustering, and pattern recognition. Large neural networks can be ...

Neural networks are composed of multiple layers, and the defining aspect of an autoencoder is that the input layers contain exactly as much information as the output layer. The reason that the input ...

An autoencoder is a type of artificial neural network commonly used to learn efficient representations of data, typically for dimensionality reduction, data compression, or denoising (noise removal).

Explainable AI 'autoencoder' with n-pt function latent space Abstract In this paper we present a novel approach to interpretable AI inspired by Quantum Field Theory (QFT) which we call the NCoder.

In this study we used deep autoencoder neural networks to construct powerful prediction models for drug-likeness and manually built three ... -tuning, the weights corresponding to the four models are ...

The autoencoder has the same number of inputs and outputs (9) as the demo program, but for simplicity the illustrated autoencoder has architecture 9-2-9 (just 2 hidden nodes) instead of the 9-6-9 ...

A neural autoencoder is essentially a complex mathematical function that predicts its input. All input must be numeric so categorical data must be encoded. Although not theoretically necessary, for ...