Elektroniknet

Instead of 2 Days: AI Analyzes Complete Brain MRIs in 3 Seconds

An AI model from Michigan analyzes MRI studies with over 30 sequences in just three seconds on a single GPU – as accurate as ...
GitHub

Attention-Guided Vector Quantized Variational Autoencoder for Brain Tumor Segmentation

Additional visualizations highlighting the comparison between the proposed two-stage AG-VQ-VAE network (without skip connections) and the single-stage AG-UNet (with skip connections) are presented.
Scientific Research Publishing

A Hybrid CNN-LSTM Variational Autoencoder for Treatment Response Prediction in Synthetic Psychiatric Data ()

Treatment response prediction remains one of the most pressing challenges in precision psychiatry, where patient heterogeneity and complex biomarker interactions limit the reliability of conventional ...
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 ...
C&EN

Mathematical Modeling of Aβ-42 Dimerization Dynamics: Integrating Physics-Based Simulations, Graph-Based Variational Autoencoder-Driven Neural Relational Inference, and Chaos ...

Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul 34349, Turkey Lab for Innovative Drugs (Lab4IND), ...
IEEE

Prior Distribution Guided Gaussian Mixture Variational Autoencoder (PDGM-VAE) for Image Generation

Abstract: Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate ...
IEEE

Deep Variational Autoencoder Based Parameter Learning of Bayesian Network with Multiple Latent Variables

Abstract: Intelligent systems could be increasingly powerful by applying probabilistic inferences over the dependence relations among observed and latent variables, which could be represented by the ...
Scientific Research Publishing

Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications ()

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 unsupervised ...