A physics-constrained AI model called VLSet-AE automates feature extraction from DRIE cross-sections with 96 percent accuracy ...
Abstract: Spiking Neural Networks (SNNs) have garnered significant attention due to their biological plausibility and energy efficiency. However, their generative modeling capabilities remain ...
Abstract: We introduce QFARE, a hybrid quantum-classical architecture for MNIST digit classification. Our approach employs a classical variational autoencoder (VAE) to compress 28×28 grayscale images ...
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 ...
ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...
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 ...
A comprehensive implementation of a Variational Autoencoder (VAE) for unsupervised data generation with uncertainty quantification, featuring comparative analysis against deterministic baselines. This ...
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