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 ...

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 ...

We propose a new algorithm, Denoising Autoencoder Classification (DAC),, which uses autoencoders, an unsupervised learning method, to improve generalization of supervised learning on limited labeled ...

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 ...

We present a novel granular computing approach that assesses landslide risk by combining fuzzy information granulation and a stacked autoencoder algorithm. The stacked autoencoder is trained using an ...

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 ...