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Self-Consistent Velocity Matching of Probability Flows

NeurIPS 2023 · Probabilistic and scientific modeling › Differential equations and PDEs · approach: Physics-informed operator learning · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use SCVM-NODE instead of adjoint method ADJNODE velocity training · Mixture of Gaussians d=10training time (minutes): 585.2 -> 7.37Helped
use SCVM-NODE instead of ADJNODE velocity training · Mixture of Gaussiansself-consistency decayHelped
use SCVM-NODE instead of JKO-ICNNgradient flow solver · Mixture of Gaussians d=60training time (minutes): 375.2 -> 23.9Helped
use SCVM-NODE instead of JKO methodsgradient flow solver · Mixture of Gaussianssymmetric KLHelped
use ADJ instead of SCVM-TIPF/NODEsolver · Ornstein-Uhlenbeck d=5, 10divergence metricsHurt
use SCVM-NODE instead of ADJsolver · Ornstein-Uhlenbeck d=10training time (minutes): 341 -> 9Helped
use SCVM-TIPF instead of SCVM-NODESCVM · Ornstein-Uhlenbeck low dimensionprobability divergencesHelped
use SCVM-TIPF instead of JKO-ICNNsolver · Porous medium equation d=6training time (minutes): 102 -> 21Helped
use SCVM-TIPF instead of JKO-ICNNsolver · Porous medium equation d>3TV distance and symmetric f-divergenceHelped
vary DFE forward Euler step sizeDFE · Ornstein-Uhlenbeck d=60Bures-Wasserstein distance: 28.11, 0.31, 0.46, 9.21Mixed
use SCVM instead of DFEsolver · Time-dependent Fokker-PlanckdivergencesHelped
use fewer coupling layers with random masking in TIPF (ablation)SCVM-TIPF · implementationaccuracyHurt

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