Figure 1. Basic Autoencoder architecture, showing encoder and decoder components [22]. Figure 2. AE-based framework for signal reconstruction, highlighting latent space compression. Recent ...

In this literature, the encoder consists of a convolutional layer and a pooling layer, while the decoder consists of only one deconvolution layer. Compared with stacked AE, CAE can retain more image ...

This paper proposes a method for predicting adolescent health risks by combining multi-sequence, two-dimensional convolutional autoencoder (2DCNN-AE) and multi-scale asynchronous correlation ...

Machine learning approaches are a powerful way to address this challenge, but they are usually tailored to only work on one specific sensor. This work addresses the challenge of transferability of ...

Convolutional autoencoder, domain adaptation, and shallow classifiers. We first separately applies NMF on MIMIC and CHOA data for feature dimensionality reduction, then used two separate CAE models to ...

Using neural networks with multiple layers, it is possible to realize the deeper learning of features contained in the data in a stepwise manner. Deep learning-based approaches, such as the deep ...