GitHub

autoencoder-mnist

Image Denoising with Autoencoders in R (University Project) Built a convolutional autoencoder in R using Keras/TensorFlow to perform image denoising on MNIST and CIFAR-10 datasets with varying levels ...
IEEE

Anomaly Detection in Time Series Data of Sensex and Nifty50 With Keras

Abstract: Anomaly detection problem for time series refers to finding outlier data points relative to some standard or usual signal. A price action that contradicts the expected movement of the stock ...
IEEE

Satellite Image Denoising Using Deep Learning: Autoencoder Decoder Application

Abstract: Through deep learning Autoencoder Decoders, it is possible to clean noisy or damaged image data received from satellites. Two models with a PSNR of 25.6 dB and 25.54 dB were generated using ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
C&EN

Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling

Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States ...
GitHub

how-to-visualize-the-encoded-state-of-an-autoencoder-with-keras.md

Autoencoders are special types of neural networks which learn to convert inputs into lower-dimensional form, after which they convert it back into the original or some related output. A variety of ...