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 ...

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 ...

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, ...

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 ...

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 ...

AI/Machine Learning; The Data Science Lab. ... To run the demo program, you must have Python and PyTorch installed on your machine. The demo programs were developed on Windows 10 using the Anaconda ...