MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, they Announced the deep optimization of stacked sparse autoencoders through the DeepSeek open ...

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 ...

In particular, the sparse autoencoder that supports GPT-4 was able to find 16 million features of GPT-4. OpenAI has published the features found from GPT-4 and GPT-2 small and the corresponding ...

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.

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.

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 ...