In this paper publicly available datasets were used together with convolutional autoencoder neural network. In preparation of dataset for training neural network a procedure for preprocessing ...

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 ...

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).

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 ...

"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 ...

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 ...

To answer the above questions, we modify a deep neural network autoencoder and we test its predictive power against two classes of approaches widely applied in recommendation systems: (a) ...

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 ...