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There is bit of difference between fitting dataset to some convenient parameterized function vs finding global minima of non-convex function. Also, paper claims
by sytelus 8y ago
There is bit of difference between fitting dataset to some convenient parameterized function vs finding global minima of non-convex function. Also, paper claims that this can be done in polynomial time.
> The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet).
- p1esk 8y agoThere is bit of difference between fitting dataset to some convenient parameterized function vs finding global minima of non-convex function What's the difference? Any point where the loss is zero is global minimum.