GitHub

A New Hybrid Model for Improving Outlier Detection Using Combined Autoencoder and Variational Autoencoder (AVE)

This repository contains the source code, scripts, and supplementary materials for the paper: "A New Hybrid Model for Improving Outlier Detection Using Combined Autoencoder and Variational Autoencoder ...
IEEE

Data Augmentation for Text Classification Using Autoencoders

Abstract: Deep learning models have greatly improved various natural language processing tasks. However, their effectiveness depends on large data sets, which can be difficult to acquire. To mitigate ...
IEEE

Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning

Abstract: In the context of histological image classification, Multiple Instance Learning (MIL) methods only require labels at Whole Slide Image (WSI) level, effectively reducing the annotation ...
Scientific Research Publishing

Faysal, A., Boushine, T., Rostami, M., Roshan, R.G., Wang, H., Muralidhar, N., Sa-hoo, A. and Yao, Y.D. (2025) Denomae: A Multimodal Autoencoder for Denoising Modulation ...

ABSTRACT: The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in ...
PNAS

ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design

The sequence of amino acids within a protein dictates its structure and function. Protein engineering campaigns seek to discover protein sequences with desired functions. Data-driven models of the ...