Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited ...
Abstract: Feature extraction and selection in the presence of nonlinear dependencies among the data is a fundamental challenge in unsupervised learning. We propose using a Gram-Schmidt (GS) type ...
Abstract: We introduce a fully unsupervised framework designed to reconstruct X-ray CT images from truncated projections without requiring prior truncation correction. By incorporating a Radon ...
Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...
ABSTRACT: Purpose: The purpose of this study is to develop a scalable, risk-aware artificial intelligence (AI) framework capable of detecting financial fraud in high-throughput digital transaction ...
AI bots told to act as trading agents in simulated markets engaged in pervasive collusion, raising new questions about how ...
Summary: New research reveals that the brain may be learning even during unstructured, aimless exploration. By recording activity in tens of thousands of neurons, scientists found that the visual ...
PCA + MiniBatch KMeans offers a strong trade-off between performance and computational cost. SAE + DBSCAN produces high-quality clusters but requires significantly more training time. Visual ...
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