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 ...

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So far, they can’t interpret all of GPT-4’s behaviors: “Currently, passing GPT-4’s activations through the sparse autoencoder results in a performance equivalent to a model trained with ...

DeepMind’s solution was to run sparse autoencoders of different sizes, varying the number of features they want the autoencoder to find. The goal was not for DeepMind’s researchers to ...

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 ...