Modern LLMs encode concepts by superimposing multiple features into the same neurons and then interpeting them by taking into account the linear superposition of all neurons in a layer. This concept ...

The project consumed over 20% of the computational resources required for training GPT-3, involved saving approximately 20 Pebibytes (PiB) of activations to disk, and resulted in hundreds of billions ...

The Sparse Autoencoder (SAE) is a type of neural network designed to efficiently learn sparse representations of data. The Sparse Autoencoder (SAE) neural network efficiently learns sparse data ...

The sparsity constraint can be implemented in various ways: The overall loss function for training a sparse autoencoder includes the reconstruction loss and the sparsity penalty: Lₜₒₜₐₗ = L( x, x̂ ) + ...

We first plot the loss function curves for miRNAs and diseases latent features based on different dimension obtained through the sparse autoencoder, respectively, as shown in Figure 2. The curve loss ...

Introducing sparse constraint (i.e., sparse autoencoder ... At the same time, we embed the class information into the loss function of autoencoder to measure intraclass similarity and improve the ...