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Amortized Mixing Coupling Processes for Clustering

NeurIPS 2022 · Learning theory and algorithms › Clustering · approach: Clustering approximation algorithms · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
AMCP instead of DACamortized clustering · Synthetic 2D GMM Scenario 1clustering accuracy: 0.7923 -> 0.8532Helped
AMCP instead of NCPamortized clustering · Synthetic 2D GMM Scenario 2clustering accuracy: 0.7233 -> 0.7574Helped
AMCP instead of ST-ACTamortized clustering · Synthetic 2D GMM Scenario 1NMI: 0.9123 -> 0.9334Helped
AMCP instead of DACamortized clustering · Tiny-ImageNetclustering accuracy: 0.0907 -> 0.1324Helped
AMCP instead of DACamortized clustering · CIFAR-10clustering accuracy: 0.8835 -> 0.9024Helped
AMCP instead of DACamortized clustering · MNISTclustering accuracy: 0.9796 -> 0.9845Helped
AMCP instead of ST-ACTamortized clustering · MNISTrunning time (s): 22.56 -> 18.22Helped
AMCP instead of ST-ACTamortized clustering · CIFAR-10running time (s): 68.32 -> 59.33Helped
amortized AMCP instead of MCMCprobabilistic clustering · Synthetic 2D GMM Scenario 1running time (s): 0.0832 -> 0.0113Helped
amortized AMCP instead of VIprobabilistic clustering · Synthetic 2D GMM Scenario 1running time (s): 0.0332 -> 0.0113Helped
increase number of EM steps T from 1 to 5 (ablation)AMCP · Scenario 1 and CIFAR-10LL and ACCHelped
increase number of EM steps T from 1 to 5 (ablation)AMCP · Scenario 1 and CIFAR-10running timeHurt

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