Learn about the most common and effective autoencoder variants for dimensionality reduction, and how they differ in structure, loss function, and application.

In this work, a sparse autoencoder controller for kinematic control of manipulators with weights obtained directly from the robot model rather than training data is proposed for the first time.

Autoencoder architecture. In the image above, AE is applied to image from MNIST dataset with size 28*28 pixels. Passing it through middle layer (also called latent space), which has 10 neurons, ...

Binary cross-entropy is appropriate for instances where the input values of the data are in a 0 – 1 range. Autoencoder Types As mentioned above, variations on the classic autoencoder architecture ...

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.

With the increasing integration of renewable energy sources into the power grid, accurate and reliable ultra-short-term forecasting of wind power is critical for optimizing grid stability and energy ...

One promising approach is the sparse autoencoder (SAE), a deep learning architecture that breaks down the complex activations of a neural network into smaller, understandable components that can ...

Sparse Autoencoder Implementation in PyTorch 👨🏽‍💻 Overview This code implements a basic sparse autoencoder (SAE) in PyTorch. The loss is implemented from scratch; it uses MSE plus a penalty using ...