After that, it utilizes both Neural Networks and Extreme Learning to compare the efficiency of machine learning algorithms. Whereas the first encoder weight matrix of the Deep Autoencoder is unfolded ...

A quantum autoencoder: using machine learning to compress qutrits Abstract: The compression of quantum data will allow increased control over difficult-to-manage quantum resources. We experimentally ...

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 ...

The result shows among the methods (support vector machine, neural network with dropout, autoencoder), neural network with added layers with dropout has the highest accuracy. And a comparison with the ...

Uncertainty quantification of machine learning (ML) predictions is of key importance for the widespread adoption of ML-enabled electromagnetic imaging. As ML inference is a predictive process, ...

Using Non-negative Matrix Factorization (NMF) and Variational Autoencoder (VAE) machine learning architectures to analyze spatial and spectral features of hyperspectral cathodoluminescence (CL) ...

In this paper, we propose a thermal machine-learning (ML) solver to speed-up thermal simulations of chips. The thermal ML-Solver is an extension of the recent novel approach, CoAEMLSim (Composable ...