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Training Neural Networks Without Gradients: A Scalable ADMM Approach

ICML 2016 · Optimization › Optimizers · approach: Constrained and non-backprop training · 140 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use ADMM instead of SGDNeural network training · SVHNtime to 95% test accuracy: 28.3 seconds -> 13.3 secondsHelped
Use ADMM instead of L-BFGSNeural network training · SVHNtime to 95% test accuracy: 3.3 seconds -> 13.3 secondsHurt
Use ADMM instead of conjugate gradientsNeural network training · SVHNtime to 95% test accuracy: 10.1 seconds -> 13.3 secondsHurt
Use ADMM instead of L-BFGSNeural network training · Higgstime to 64% test accuracy: 181 seconds -> 7.8 secondsHelped
Use ADMM instead of conjugate gradientsNeural network training · Higgstime to 64% test accuracy: 44 minutes -> 7.8 secondsHelped
Use ADMM instead of SGDNeural network training · Higgsreaching 64% test accuracyHelped
Use L-BFGS instead of ADMMNeural network training · Higgstest accuracy: 75%Helped

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