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 ...

The NSAE-SU model is an unsupervised deep learning model autoencoder. The NSAE-SU is programmed in Python using the TensorFlow libraries; the encoder and decoder integrate the model. The abundance map ...

By leveraging convolutional neural networks and molecular dynamics simulations, we have developed a denoising autoencoder (DAE) capable of postprocessing experimental ChromSTEM images to provide ...

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 ...

This research represents a versatile open-source python library (Pythae). This library’s main goal is to provide a uniform implementation and a specialized framework for using generative autoencoder ...