Scientists at Weill Cornell Medicine have developed a new algorithm, the Krakencoder, that merges multiple types of brain ...

Using an algorithm they call the Krakencoder, researchers at Weill Cornell Medicine are a step closer to unraveling how the brain's wiring supports the way we think and act. The study, published June ...

Abstract: In this article, we mainly study the depth and width of autoencoders consisting of rectified linear unit (ReLU) activation functions. An autoencoder is a layered neural network consisting of ...

To achieve this, the study applied the Fusion of Activation Functions (FAFs) to a substantial dataset. This dataset included 307,594 container records from the Port of Tema from 2014 to 2022, ...

Behind the scenes, the autoencoder uses tanh() activation on the hidden nodes and tanh() activation on the output nodes. The result of the tanh() function is always between -1 and +1. Therefore, the ...

The work raises the question whether modern activation functions that are rectified linear unit shaped are beneficial in unsupervised models. We evaluate our approach in autoencoder structures on ...

We use 'sigmoid' as activation function for decoder layer because we want a binary result. Deep fully-connected autoencoder: Instead of using one layer for encoder model and decoder model respectively ...

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and ...

Many of the autoencoder examples I see online use relu() activation for interior layers. The relu() function was designed for use with very deep neural architectures. For autoencoders, which are ...