BriefGPT.xyz
Jan, 2019
正则化线性自编码器的损失景观
Loss Landscapes of Regularized Linear Autoencoders
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Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed
TL;DR
证明了 L2 正则化线性自编码器在所有临界点处均对称并学习到解码器的左奇异向量作为主方向,相关结果说明了主成分分析算法、计算神经科学和学习的代数拓扑性质。
Abstract
autoencoders
are a deep learning model for
representation learning
. When trained to minimize the Euclidean distance between the data and its reconstruction, linear
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