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Jul, 2021
KOALA: 具有损失适应性的卡尔曼优化算法
KaFiStO: A Kalman Filtering Framework for Stochastic Optimization
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Aram Davtyan, Sepehr Sameni, Llukman Cerkezi, Givi Meishvilli, Adam Bielski...
TL;DR
该研究提出了一种基于卡尔曼滤波器的随机优化算法 KOALA,其将损失函数视为相对于某个理想值的噪声观测,以估计未知参数,并展示了其在训练神经网络方面的高效性和可扩展性。
Abstract
optimization
is often cast as a deterministic problem, where the solution is found through some iterative procedure such as gradient descent. However, when training
neural networks
the loss function changes over
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