Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning

7 Apr 2019Kenji KawaguchiJiaoyang HuangLeslie Pack Kaelbling

For nonconvex optimization in machine learning, this article proves that every local minimum achieves the globally optimal value of the perturbable gradient basis model at any differentiable point. As a result, nonconvex machine learning is theoretically as supported as convex machine learning with a handcrafted basis in terms of the loss at differentiable local minima, except in the case when a preference is given to the handcrafted basis over the perturbable gradient basis... (read more)

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