In this paper, we describe a phenomenon, which we named "super-convergence", where neural networks can be trained an order of magnitude faster than with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well. One of the key elements of super-converge…
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3 results for “super-convergence”
Optimal Deep Neural Network Approximation for Korobov Functions with respect to Sobolev Normsmath.NA
Paper shows deep neural networks can approximate Korobov functions nearly optimally.
problem Approximating Korobov functions with deep neural networks.
method Used deep neural networks and measured approximation rates with and norms.
result Achieved a super-convergence rate, outperforming traditional methods.
Stationary MMD Pointsstat.ML
New method finds points for approximating distributions faster.
problem Approximating target probability distributions using finite points.
method Stationary MMD points computed via MMD gradient flows.
result Stationary MMD points converge faster than global minimizers.