This paper proposes an autoencoder based multiple-input multiple-output (MIMO) communication system. The proposed autoencoder learns and optimizes for only line of sight (LOS) component of Rician ...

However, existing approaches have limitations in capturing temporal dependencies and analyzing frequency domain relationships among pollutants. In this study, we propose a novel multi-input model ...

micae-experiments Collection of experiments on multiple-input convolutional autoencoder neural networks This repository contains the collection of studies and explorations I undertook for part of my ...

Deep_Multi_Modal_Autoencoder The code used for the IEEE International Conference on Acoustics, Speech, and Signal Processing 2019 submission This code is used for performing feature set pre-processing ...

Autoencoders are neural networks. 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 ...

Masked autoencoders (MAEs) are a self-supervised pretraining strategy for vision transformers (ViTs) that masks-out patches in an input image and then predicts the missing regions. Although the ...