Massachusetts Institute of Technology Details Findings in Machine Learning (Kan- odes: Kolmogorov-arnold Network Ordinary Differential Equations for Learning Dyn amical Systems and Hidden Physics)
Massachusetts Institute of Technology Details Findings in Machine Learning (Kan- odes: Kolmogorov-arnold Network Ordinary Differential Equations for Learning Dyn amical Systems and Hidden Physics)
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻每日新闻-机器学习的新数据在一份新的报告中呈现。根据来自马萨诸塞州剑桥的新闻报道，由NewsRx记者报道，研究称，“kolmogorov-arnold net works(KANs)可替代多层感知器(MLPs)”是最近推出的显示出数据驱动建模的强大潜力的开发。这个工作应用程序欺骗了KANs神经常微分方程(ODE)框架的骨干，推广其应用到动力学系统和科学机器中常见的时变网格敏感学习应用S."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine LearningDaily News Daily News – Fresh data on Machine Learning are pre sented in a new report. Accordingto news reporting originating from Cambridge, Massachusetts, by NewsRx correspondents, research stated,“Kolmogorov-Arnold net works (KANs) as an alternative to multi-layer perceptrons (MLPs) are a recentde velopment demonstrating strong potential for data-driven modeling. This work app lies KANs asthe backbone of a neural ordinary differential equation (ODE) frame work, generalizing their use to thetime-dependent and temporal grid-sensitive c ases often seen in dynamical systems and scientific machinelearning application s.”
Key words
Cambridge/Massachusetts/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning/Ma ssachusetts Institute of Technology
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