IEEE

Representation Learning With Dual Autoencoder for Multi-Label Classification

Abstract: Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label ...
IEEE

Vector representation of non-standard spellings using dynamic time warping and a denoising autoencoder

Abstract: The presence of non-standard spellings in Twitter causes challenges for many natural language processing tasks. Traditional approaches mainly regard the problem as a translation, spell ...
GitHub

scVAG: Unified Single-Cell Clustering via Variational-Autoencoder Integration with Graph Attention Autoencoder

scVAG is an innovative framework that integrates Variational Autoencoder (VAE) and Graph Attention Autoencoder (GATE) models for enhanced analysis of single-cell gene expression data. Built upon the ...

Novel AI platform accelerates discovery of chemical catalysts for industry

CatDRX is a generative AI framework developed at Institute of Science Tokyo, which enables the design of new chemical ...
AZoSensors

AI Maps Heat Inside Steelmaking’s Critical Sintering Process Beds

The Temporal Fusion Transformer model provides near-real-time insights into sintering temperatures, addressing critical ...
GitHub

Variational Autoencoder (VAE) for Image Generation

This project implements a Variational Autoencoder (VAE) for image generation. Unlike standard autoencoders, VAE learns a probabilistic latent space by encoding images to a distribution and sampling ...