首页|University of Porto Researchers Yield New Study Findings on Machine Learning (The world in a grain of sand: Condensing the string vacuum degeneracy)
University of Porto Researchers Yield New Study Findings on Machine Learning (The world in a grain of sand: Condensing the string vacuum degeneracy)
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Elsevier
Research findings on artificial intelligence are discussed in a new report. According to news reporting out of Porto, Portugal, by NewsRx editors, research stated, “The proliferation of vacuum solutions to string theory highly deter the search for the Standard Model.” Financial supporters for this research include Stfc. Our news reporters obtained a quote from the research from University of Porto: “We propose a novel approach to this problem by finding an efficient measure of similarity of vacua. Using one million concrete examples, the paradigm of few-shot machine-learning represents them as points in Euclidean three-space with similar points clustered together. We thereby compress the search space for desired physics to within one percent of the original.”
University of PortoPortoPortugalEuropeCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Elsevier
