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Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels

ICML 2023 · Optimization › Optimizers · approach: Stein variational gradient descent · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Neural forward scheme instead of neural backward schemeinteraction energy flow with Riesz kernel r=1 · d=2 benchmark against analytic flowdiscrepancy D2K to analytic flowHelped
Neural backward scheme instead of particle flowinteraction energy flow with Riesz kernel r=1.5 · d=2 benchmark against analytic flowdiscrepancy D2K to analytic flowHelped
Neural backward scheme vs particle flow for Riesz r=0.5interaction energy flow with Riesz kernel r=0.5 · d=2 benchmark against analytic flowdiscrepancy D2K to analytic flowNo clear effect
Particle flow instead of neural schemes in high dimensioninteraction energy flow with r=1, d=1000 and d=10 · benchmark against analytic flowdiscrepancy D2K to analytic flowHelped
Particle flow instead of neural schemes in 3Dinteraction energy flow with r=1, d=3 · benchmark against analytic flowdiscrepancy D2K to analytic flowHurt
Neural forward scheme requires r=1 (not defined for other r)neural forward scheme · interaction energy flow benchmarkwell-definednessHurt

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