Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
Huangyu Xu ⋅ Jingqin Yang ⋅ Qianqian Xu ⋅ Jiaye Teng
Abstract
Sparse optimization is a fundamental challenge in various practical applications. A popular approach to sparse optimization is Lp regularization. However, it may encounter optimization instability due to the unbounded gradients when 0
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