SGD Converges to Global Minimum in Deep Learning via Star-convex Path

ICLR 2019 Yi ZhouJunjie YangHuishuai ZhangYingbin LiangVahid Tarokh

Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how and why SGD can train these complex networks towards a global minimum... (read more)

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