Classical momentum acceleration in mini-batch SGD for quadratics is proportional to batch size up to saturation, enabling perfect parallelization under minimal noise assumptions.
Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830
2 Pith papers cite this work. Polarity classification is still indexing.
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Benchmark study finds quantile and z-score marking strategies most robust for adaptive mesh refinement in steady mechanics problems, with Dörfler effective at large parameters and Isolation Forest competitive only under generous settings.
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Perfect Parallelization in Mini-Batch SGD with Classical Momentum Acceleration
Classical momentum acceleration in mini-batch SGD for quadratics is proportional to batch size up to saturation, enabling perfect parallelization under minimal noise assumptions.
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Marking strategies for adaptive mesh refinement: An efficiency-focused benchmark study for steady solid and fluid mechanics problems
Benchmark study finds quantile and z-score marking strategies most robust for adaptive mesh refinement in steady mechanics problems, with Dörfler effective at large parameters and Isolation Forest competitive only under generous settings.