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HyperTransfer is proposed, which constructs a Hyperball optimizer that reproduces the dynamics of a target Base Optimizer using only its initialization and learning-rate schedule, without running the target optimizer itself, to extend the framework to non-scale-invariant networks.
This work relaxes the min cut problem into a double-bounded constraint and, for the first time, treats the min cut problem as a double-bounded nonlinear optimal transport problem and develops a method for solving double bounded nonlinear optimal transport based on the Frank-Wolfe method.
The Marcus mapping is extended, which indicates that certain sparse matrices can also be transformed into doubly stochastic symmetric matrices via diagonal matrices, and it is proved that the Marcus mapping solves a specific type of optimal transport problem.
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