One promising approach is the sparse autoencoder (SAE), a deep learning architecture that breaks down the complex activations of a neural network into smaller, understandable components that can ...

The stacked sparse autoencoder is a powerful deep learning architecture composed of multiple autoencoder layers, with each layer responsible for extracting features at different levels.

Features are produced by sparse autoencoders, which are a type of neural network architecture. During the AI training process, sparse autoencoders are guided by, among other things, scaling laws.