UC Santa Cruz researchers are exploring how brains learn, adapt, and improve, which could help us better understand and address neurological conditions.
With the introduction of adaptive deep brain stimulation (aDBS) for Parkinson's disease, new questions emerge regarding who, why, and how to treat. This paper outlines the pathophysiological rationale ...
This study presents a bio-inspired control framework for soft robots, enhancing tracking accuracy by over 44% under disturbances while maintaining stability.
Adaptive systems were supposed to simplify decision-making. Instead of hard-coded rules, engineers built models that could learn from data, respond to change, and improve over time. That promise still ...
This repository is publicly available on GitHub and provides full access to the source code required to reproduce all experiments reported in the manuscript. The source code has been archived on ...
Abstract: Optimization methods often face a trade-off between the fast convergence of second-order methods and the low computational cost of first-order methods. Motivated by the need to bridge this ...
ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
1 College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong Province, China 2 Shandong Gaomi Technician Institute, Shandong Province, China For the low ...
School of Textiles and Fashion, Shanghai University of Engineering Science, Shanghai 201620, PR China Key Laboratory of Cleaning Dyeing and Finishing Technology of Zhejiang Province, Shaoxing ...
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