Essentially all cells in an organism's body have the same genetic blueprint, or genome, but the set of genes that are actively expressed at any given time in a cell determines what type of cell it ...

Autoencoder networks: the core of the attentional autoencoder network is the autoencoder. An autoencoder is a neural network structure that consists of an encoder and a decoder.

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

The autoencoder network model for HIV classification, proposed in this paper, thus outperforms the conventional feedforward neural network models and is a much better classifier. Current Science is a ...

Ziwei Zhu, Assistant Professor, Computer Science, College of Engineering and Computing (CEC), received funding for the project: “III: Small: Harnessing Interpretable Neuro-Symbolic Learning for ...

The neural autoencoder anomaly detection technique presented in this article is just one of many ways to look for data anomalies. The technique assumes you are working with tabular data, such as log ...