Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Herein, we present Rapidae, an open-source Python library designed to ease the use, development, and benchmarking of autoencoder models. Rapidae is oriented towards precise and imprecise data, mainly ...

Abstract: Autoencoders have proven successful across diverse applications such as data reconstruction, anomaly detection, and feature extraction, however, these advancements remain largely dispersed ...

An autoencoder is an unsupervised deep neural network that has learned the structure of the data and performs feature extraction due to a latent data representation. ... The NSAE-SU is programmed in ...

Scanning transmission electron microscopy tomography with ChromEM staining (ChromSTEM), has allowed for the three-dimensional study of genome organization. By leveraging convolutional neural networks ...

This repository contains the python code for the Autoencoder Based Iterative Modeling and Subsequence Clustering Algorithm (ABIMCA) 1 which is a deep learning method to separate multivariate ...

It is widely used in voice modeling, clustering, and data augmentation applications. This research represents a versatile open-source python library (Pythae). This library’s main goal is to provide a ...