The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks

NeurIPS 2019 Ryo KarakidaShotaro AkahoShun-ichi Amari

Normalization methods play an important role in enhancing the performance of deep learning while their theoretical understandings have been limited. To theoretically elucidate the effectiveness of normalization, we quantify the geometry of the parameter space determined by the Fisher information matrix (FIM), which also corresponds to the local shape of the loss landscape under certain conditions... (read more)

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