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For example—consider an autoencoder that has been trained on a specific dataset P. For any image sampled for the training dataset, the autoencoder is bound to give a low reconstruction loss and ...

The Autoencoder is trained on normal operational data, and when it encounters anomalous data, it produces a large reconstruction error, which helps to identify the anomalies. Figure 1: An AI-generated ...

A new autoencoder dealing with interval-valued or set-valued training data is studied in the paper. The first main idea underlying the autoencoder is based on t ...

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 ...

By: Katherine C. Kellogg, Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon and Karim R. Lakhani ...

Incorporating detailed chemical kinetic models is critical for accurate simulations of reacting flows. However, detailed models involve a large number of thermochemical (TC) state variables. Solving ...