GitHub

TensorFlow Graphics

The last few years have seen a rise in novel differentiable graphics layers which can be inserted in neural network architectures. From spatial transformers to differentiable graphics renderers, these ...
GitHub

Variational Autoencoder in tensorflow and pytorch

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 ...
IEEE

Examining the Utility of Differentially Private Synthetic Data Generated using Variational Autoencoder with TensorFlow Privacy

Abstract: With the emergence of AI(artificial intelligence), it is becoming more and more critical for organizations to utilize it to their advantage. However, organizations that possess a decent ...
C&EN

DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
IEEE

Defending Deep Neural Networks Against Backdoor Attack by Using De-Trigger Autoencoder

Abstract: A backdoor attack is a method that causes misrecognition in a deep neural network by training it on additional data that have a specific trigger. The network will correctly recognize normal ...
Frontiers

Explore Protein Conformational Space With Variational Autoencoder

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational ...
Frontiers

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology performed at the level of an individual cell, which can have a potential to understand cellular heterogeneity. However ...