Limitation of characterizing implicit regularization by data-independent functions
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In recent years, understanding the implicit regularization of neural networks (NNs) has become a central task of deep learning theory. However, implicit regularization is in itself not completely defined and well understood. In this work, we make an attempt to mathematically define and study the implicit regularization. Importantly, we explore the limitation of a common approach of characterizing the implicit regularization by data-independent functions. We propose two dynamical mechanisms, i.e., Two-point and One-point Overlapping mechanisms, based on which we provide two recipes for producing classes of one-hidden-neuron NNs that provably cannot be fully characterized by a type of or all data-independent functions. Our results signify the profound data-dependency of implicit regularization in general, inspiring us to study in detail the data-dependency of NN implicit regularization in the future.
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