This paper proposes a novel hybrid model that synergistically integrates Variational Autoencoders (VAEs) and Speeded-Up Robust Features (SURF) to address these challenges. The VAE component captures ...

SpaCAE (SPAtially Contrastive variational AutoEncoder) is a spatially contrastive variational ... including identifying spatial domains by Mclust algorithms, denoising the SRT profiles with ...

Recently, we developed a novel nonlinear analysis framework for adult rsfMRI using unsupervised deep generative model – variational autoencoder (VAE ... The age prediction algorithm was identical to ...

For instance, if we want to produce new artificial images of cats, we can use a variational autoencoder algorithm to do so, after training on a large dataset of images of cats. The input dataset is ...

Abstract: We present an inversion algorithm with a deep-learning-based model compression scheme. Models are described with latent parameters of a trained variational autoencoder (VAE) neural network.

The demo sets up training parameters for the batch size (10), number of epochs to train (100), loss function (mean squared error), optimization algorithm ... to complement an autoencoder with an ...

In order to effectively model this sequential data and predict electricity consumption accurately, we propose a multi-scale prediction (Long Short Term Memory, LSTM) algorithm based on Time-Frequency ...

Using the segmentation algorithms described by Moskalenko et al ... and 24 feature, respectively). The variational autoencoder models for each lead were also trained. Examples of the results of ...