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Apr, 2024
楼梯滑行:相关潜变量如何加速神经网络的学习
Sliding down the stairs: how correlated latent variables accelerate learning with neural networks
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Lorenzo Bardone, Sebastian Goldt
TL;DR
神经网络从高阶输入累积量中有效地提取相关方向并通过层次性学习加速了模型的性能。
Abstract
neural networks
extract features from data using stochastic gradient descent (SGD). In particular,
higher-order input cumulants
(HOCs) are crucial for their performance. However, extracting information from the $
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