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.

Sparse autoencoders (SAE) use the concept of autoencoder with a slight modification. During the encoding phase, the SAE is forced to only activate a small number of the neurons in the intermediate ...

Features are produced by sparse autoencoders, which are a type of neural network architecture. ... including a 16 million feature autoencoder on GPT-4,” OpenAI wrote.

To find features—or categories of data that represent a larger concept—in its AI model, Gemma, DeepMind ran a tool known as a “sparse autoencoder” on each of its layers.

A sparse autoencoder is, essentially, a second, smaller neural network that is trained on the activity of an LLM, looking for distinct patterns in activity when “sparse” (ie, very small ...

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. HOLO utilizes ...