Microsoft

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in a low-dimensional ...
IEEE

Hyperbolic Space-Based Autoencoder for Hyperspectral Anomaly Detection

Abstract: Deep-learning (DL)-based methods have been shown to be effective on the hyperspectral image (HSI) anomaly detection task because of their feature extraction ability. However, current ...
IEEE

AEGANAuth: Autoencoder GAN-Based Continuous Authentication With Conditional Variational Autoencoder Generative Adversarial Network

Abstract: In recent years, sensor-based continuous authentication on mobile devices has proven highly effective in safeguarding personal information. However, these proposed approaches often require ...
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

Advanced Image Reconstruction Autoencoders

This project explores advanced image reconstruction techniques utilizing autoencoder neural networks. Autoencoders are a type of artificial neural network particularly useful for unsupervised learning ...
GitHub

rzhao-zhsq/CV-SLT

We propose CV-SLT to facilitate direct and sufficient cross-modal alignment between sign language videos and spoken language text. Specifically, our CV-SLT consists of two paths with two KL ...
Frontiers

An autoencoder-based deep learning method for genotype imputation

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta ...