Take each of the pre-trained RBMs and stack them to create a deep autoencoder. Each RBM will show up twice in the autoencoder, once as an encoder, and once as a decoder. Finally, fine-tune the ...

Autoencoder.py defines a class that pretrains and unrolls a deep autoencoder, as described in "Reducing the Dimensionality of Data with Neural Networks" by Hinton and Salakhutdinov. The layer ...

Abstract: This paper presents a comparison performance on three types of autoencoders, namely, the traditional autoencoder with Restricted Boltzmann Machine (RBM), the stacked autoencoder without RBM ...

Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break down ...

We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing ... We further show how generating samples from the D-Wave after training the RBM, ...

Abstract: This paper presents a comparison performance on three types of autoencoders, namely, the traditional autoencoder with Restricted Boltzmann Machine (RBM), the stacked autoencoder without RBM ...