Oct 10, 2024 · Abstract: Sparse autoencoders have recently produced dictionaries of high-dimensional vectors corresponding to the universe of concepts represented by large language …

Apr 8, 2025 · This is an implementation that shows how to construct a sparse autoencoder with TensorFlow and Keras in order to learn useful representations of the MNIST dataset. The …

Examples are regularized autoencoders (sparse, denoising and contractive autoencoders), which are effective in learning representations for subsequent classification tasks, [3] and variational …

Mar 27, 2025 · SAE feature structure: Sparse autoencoders (SAEs) are a recent approach for discovering interpretable language model features without supervision, although relatively few …

We will first describe feedforward neural networks and the backpropagation algorithm for supervised learning. Then, we show how this is used to construct an autoencoder, which is an …

We develop a state-of-the-art methodology to reliably train extremely wide and sparse autoencoders with very few dead latents on the activations of any language model. We …

A Sparse Autoencoder is a type of autoencoder that employs sparsity to achieve an information bottleneck. Specifically the loss function is constructed so that activations are penalized within …

Dec 4, 2018 · The difference between a basic autoencoder and neural networks is that autoencoder is composed of two symmetric parts: encoder and decoder with dimensions of …

May 3, 2022 · This article will focus on Sparse Autoencoders (SAE) and compare them to **** Undercomplete Autoencoders (AE). Contents. SAE within the universe of Machine Learning …

In this notebook, we will explore one of the cutting-edge approaches to interpreting superposition: sparse autoencoders (SAE). SAEs are a type of neural network used in unsupervised learning …