Deep learning has received leapfrog progress in the realm of Machine learning such as image processing, speech recognition, the natural language processing, and recommendation systems. The traditional ...

The autoencoder algorithm achieved the best anomaly detection performance than the other benchmark ... Dou T, Clasie B, Depauw N, Shen T, Brett R, Lu HM, et al. A deep LSTM autoencoder-based framework ...

Issue 1: Training an AE by a stochastic gradient descent (SGD)-based backward propagation algorithm demands one or more differentiable channel model layers that connect the deep neural layers in the ...

In this study, k-means clustering was performed on the data set using raw data and PCA-extracted and deep autoencoder-extracted features.The performance of the clusters was compared between each input ...

It performs a Deep Autoencoder model with with a specified model. After that, it utilizes both Neural Networks and Extreme Learning to compare the efficiency of machine learning algorithms. Whereas ...

Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...

This project implements the DAC (Deep Autoencoder-based Clustering) framework as described in the paper titled "DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of ...

The researchers tested the algorithm's performance against the diagnoses of 21 dermatologists from the Stanford medical school, on three critical diagnostic tasks: keratinocyte carcinoma ...

The team adapted an existing deep-learning algorithm known as a convolutional variational autoencoder (CVAE), which automatically extracted relevant information about protein folding ...