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Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization

AAAI 2023 · Optimization › Nonconvex and smooth optimization · approach: Stochastic convex optimization complexity · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace the v1-SG estimator with the v1-SVRG estimator (ablation)DEEPSTORM · a9atest accuracy: 84.96±1235.0e-4 -> 84.94±929.04e-4No clear effect
Use the v2 estimator instead of v1-SG (ablation)DEEPSTORM · a9atest accuracy: 84.96±1235.0e-4 -> 85.11±478.03e-4No clear effect
Replace the v1-SG estimator with the v1-SVRG estimator (ablation)DEEPSTORM · MiniBooNEtest accuracy: 84.24±1902.0e-4 -> 84.15±1974.0e-4No clear effect
Use the v2 estimator instead of v1-SG (ablation)DEEPSTORM · MiniBooNEtest accuracy: 84.24±1902.0e-4 -> 84.25±1752.0e-4No clear effect
Replace the v1-SG estimator with the v1-SVRG estimator (ablation)DEEPSTORM · MNISTtest accuracy: 97.97±1261.0e-4 -> 98.08±1485.0e-4No clear effect
Use the v2 estimator instead of v1-SG (ablation)DEEPSTORM · MNISTtest accuracy: 97.97±1261.0e-4 -> 98.15±659.04e-4No clear effect
Use DEEPSTORM v1-SG instead of DSGTDEEPSTORM · a9atest accuracy: 84.89±271.02e-4 -> 84.96±1235.0e-4No clear effect
Use DEEPSTORM v1-SG instead of DSGTDEEPSTORM · MiniBooNEtest accuracy: 84.24±202.07e-4 -> 84.24±1902.0e-4No clear effect
Use DEEPSTORM v1-SG instead of DSGTDEEPSTORM · MNISTtest accuracy: 97.61±1346.0e-4 -> 97.97±1261.0e-4No clear effect

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