The traditional recommendation algorithm has existed the matter of cold start and data sparsity. To alleviate such problems, we propose a deep autoencoder algorithm for collaborative filtering ...

However, due to the characteristics of real-time transmission and large amounts of data, there is an urgent need for efficient and lightweight EEG compression algorithms ... reconstruction channels ...

To address this issue, in this paper, we propose a scRNA-seq data dimensionality reduction algorithm based on a hierarchical autoencoder, termed SCDRHA. The proposed SCDRHA consists of two core ...

This project contains an Autoencoder, built and trained using Tensorflow, and used to vectorize images, so a kNN algorithm can check for image similarity. It contains two major Python notebooks, one ...

Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier ...

To develop their model, the researchers used a machine learning algorithm called an autoencoder, which automatically integrates gigantic swaths of data into a concise representation—a simpler ...