A study reveals random exploration outperforms focused analysis—shedding scientific light on non-ordinary ways of knowing.

Advances in ultrasound—the same imaging technology that uses sound waves to allow doctors to monitor babies in utero—are being applied by engineers at the University of California San Diego to make ...

Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

In this talk, I will present a series of new results in supervised learning from contaminated datasets, based on a general outlier removal algorithm inspired by recent work on learning with ...

Abstract: Although deep reinforcement learning (DRL) has made massive progress in policy learning, its reliance on a large number of real-world data samples presents a significant barrier to broader ...

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 ...

Abstract: Cross-scene hyperspectral image classification has achieved favorable outcomes in the domain adaptation of deep learning. However, transferring the sample features learned from the source ...