2021/07/14 by Martin Svedin, Svedin, Martin, Artur Podobas +5
Computer Science · #Advanced Data Storage Technologies #Advanced Neural Network Applications #Computational Engineering #Distributed #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Parallel #Parallel Computing and Optimization Techniques #and Cluster Computing (cs.DC) #and Science (cs.CE) #cs.CE #cs.DC #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.2107.06676
Accepted for publication at The 2nd Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S 2021)
openalex publication_date 2021/07/14 · arxiv created 2021/08/17 · arxiv updated 2021/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the most promising approaches for data analysis and exploration of large data sets is Machine Learning techniques that are inspired by brain models. Such methods use alternative learning rules potentially more efficiently than established learning rules. In this work, we focus on the potential of brain-inspired ML for exploiting High-Performance Computing (HPC) resources to solve ML problems: we discuss the BCPNN and an HPC implementation, called StreamBrain, its computational cost, suitability to HPC systems. As an example, we use StreamBrain to analyze the Higgs Boson dataset from High Energy Physics and discriminate between background and signal classes in collisions of high-energy particle colliders. Overall, we reach up to 69.15% accuracy and 76.4% Area Under the Curve (AUC) performance.