Abstract: Electromyographic (EMG) signals serve as critical indicators of neuromuscular dynamics, with growing applications in clinical diagnostics and wearable neuroprosthetics. Despite their ...
Abstract: Many modern classification problems involve data that live in high-dimensional spaces but exhibit strong low-dimensional structure. Motivated by the manifold hypothesis, this talk presents a ...
Abstract: In deep brain stimulation (DBS) surgery for Parkinson’s disease (PD), the accurate intraoperative identification of key nuclei—such as the subthalamic nucleus (STN)—is critical to ensuring ...
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 ...
Last week, Tesla announced its long-gestating, highly controversial Robotaxis would start operating in Austin "unsupervised." (In other words, without a human safety monitor inside the car.) The ...
ABSTRACT: Context and Justification: As financial services undergo accelerated digitalization, the expansion of electronic transactions within digital wallets increases vulnerabilities to fraud, ...
A comprehensive, standalone educational resource for learning remote sensing and digital image processing using Google Earth Engine. This course was originally developed at the University of Florida ...
We consider learning a sequence classifier without labeled data by using sequential output statistics. The problem is highly valuable since obtaining labels in training data is often costly, while the ...
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