This paper proposes a learning-based approach for reconstruction of global illumination with very low sampling budgets (as low as 1 spp) at interactive rates. At 1 sample per pixel (spp), the Monte ...

For an autoencoder anomaly detection system, model overfitting is characterized by a situation where all reconstructed inputs match the source inputs very closely, and therefore all reconstruction ...

Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk ...

“In the reconstruction ratio, the autoencoder takes the place of the PCA,” he adds. Autoencoders are not new in finance. They are designed to recognise patterns and identify exceptions, and have been ...

Detecting changes in asset co-movement using the autoencoder reconstruction ratio ARR aims to anticipate volatility patterns to provide signals for risk management and trading ...