There exists many ways to detect anomaly, One-class SVMs, Elliptic Envelopes... These methods belong to the field of machine learning, however there are also many models for anomaly detection in deep ...

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 ...

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 ...

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) ...

Machine learning is one of the quickest growing technological fields, ... The goal of an autoencoder is to convert the input data and rebuild it as accurately as possible, so it's in the incentive of ...

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 ...

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 ...