Implementing a deep autoencoder is possible but requires a lot of effort. A result from the Universal Approximation Theorem (sometimes called the Cybenko Theorem) states, loosely speaking, that a ...

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 ...

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.

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 ...