Arbitrary heterogeneous fan-in profiles in sparse networks match uniform random accuracy at high sparsity, but initializing RigL dynamic sparse training with equilibrium-matched lognormal profiles improves performance by up to 0.49% on classification tasks.
Sparser, better, deeper, stronger: Improving sparse training with exact orthogonal initialization
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Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria
Arbitrary heterogeneous fan-in profiles in sparse networks match uniform random accuracy at high sparsity, but initializing RigL dynamic sparse training with equilibrium-matched lognormal profiles improves performance by up to 0.49% on classification tasks.