In addition, comparisons are performed against the conventional OTFS system with state-of-the-art signal detectors for HI-compensation, based on convolutional neural network (CNN), and it is found ...

To address this issue, we proposed an intelligent HB design method based on the autoencoder (AE) neural network in this paper. By mapping the HB system to an AE neural network, the solving of the ...

Incorporating detailed chemical kinetic models is critical for accurate simulations of reacting flows. However, detailed models involve a large number of thermochemical (TC) state variables. Solving ...

An artificial neural network called an autoencoder is used to learn effective codings for unlabeled input (unsupervised learning). By teaching the network to disregard irrelevant data (or “noise”), ...

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).

VAE on Simple Autoencoder This repository contains the implementation of Variational Autoencoders (VAEs) using simple autoencoder architectures on various datasets. VAEs are a type of neural network ...

"The neural network was trained to output stimuli that, when fed through the sensory model, achieve the desired target response. Thus, the system is a hybrid autoencoder, where the encoder is a ...