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Active Mini-Batch Sampling Using Repulsive Point Processes

AAAI 2019 · Optimization › Optimizers · approach: Stochastic gradient MCMC · 35 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use repulsive Poisson Disk sampling instead of standard random mini-batch samplingSGD · MNISTtest error rateHelped
Use repulsive Poisson Disk sampling instead of standard random mini-batch samplingSGD · Speech Command Recognitionvalidation accuracyHelped
Use Vanilla PDS instead of standard SGDSGD · Oxford Flowertest accuracyHelped
Use Poisson Disk sampling instead of k-DPPOxford Flower mini-batch sampling · Oxford Flowersampling time (sec): 7.1680 -> 0.0461; 29.687 -> 0.0795; 58.4086 -> 0.1048; 189.0303 -> 0.1657; 436.4746 -> 0.2391Helped
Use Poisson Disk sampling instead of Fast k-DPPOxford Flower mini-batch sampling · Oxford Flowersampling time (sec): 0.1032 -> 0.0461; 0.3312 -> 0.0795; 0.6512 -> 0.1048; 1.8745 -> 0.1657; 4.1964 -> 0.2391Helped
Add adaptive repulsion that exempts points near decision boundaries (ablation)Vanilla PDS · MNISTtest error rateNo clear effect
Bias sampling toward points with higher mingling indices (ablation)Vanilla PDS (Dense PDS) · MNISTtest error rateHelped
Anneal sampling density from easy examples toward higher-mingling examples (ablation)Dense PDS (Anneal PDS) · MNISTtest error rateHelped
Add adaptive repulsion that exempts points with nonzero mingling indices (ablation)Vanilla PDS (Easy PDS) · Speech Command Recognitionvalidation accuracyNo clear effect
Bias sampling toward points with higher mingling indices (ablation)Vanilla PDS (Dense PDS) · Speech Command Recognitionvalidation accuracyNo clear effect

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