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A Retrospective Approximation Approach for Smooth Stochastic Optimization
Our investigation leads naturally to generalizing SG into Retrospective Approximation (RA) where, during each iteration, a "deterministic solver" executes possibly multiple steps on a subsampled deterministic problem and stops when further solving is deemed unnecessary from the standpoint of statistical efficiency.
Towards an Understanding of Residual Networks Using Neural Tangent Hierarchy (NTH)
Gradient descent yields zero training loss in polynomial time for deep neural networks despite non-convex nature of the objective function.
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