When designing an autoencoder, machine learning engineers need to pay attention to four different model hyperparameters: code size, layer number, nodes per layer, and loss function. The code size ...

It performs a Deep Autoencoder model with with a specified model. After that, it utilizes both Neural Networks and Extreme Learning to compare the efficiency of machine learning algorithms. Whereas ...

Abstract: The compression of quantum data will allow increased control over difficult-to-manage quantum resources. We experimentally realize a quantum autoencoder ...

The demo sets up training parameters for the batch size (10), number of epochs to train (100), loss function (mean squared error), optimization algorithm (stochastic gradient descent) and learning ...

Machine learning is one of the quickest growing technological fields ... basically creating their own labeled training data. The goal of an autoencoder is to convert the input data and rebuild it as ...

Abstract: Uncertainty quantification of machine learning (ML) predictions is of key importance ... Monte-Carlo Dropout Bayesian convolutional neural network (BCNN) with an autoencoder (AE) to solve ...

The .h5py hyperspectral datasets are not publicly available at this time as they have not yet been published. However, when they are published, they will be included ...