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Research map / Probabilistic and scientific modeling / Variational and generative sampling

Path-Gradient Estimators for Continuous Normalizing Flows

ICML 2022 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Normalizing flow training and design · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
path-gradient estimator replacing total derivative for CNF trainingEquivariant CNF for phi^4 lattice field theory · Lattice 12x12 to 32x32ESS: +1.65 (12x12), +6.1 (20x20), +22.3 (32x32) pointsHelped
path-gradient estimator replacing total derivative for FFJORD VAE trainingFFJORD VAE · MNIST, Omniglot, Caltech Silhouettes, Frey Facesnegative ELBO: lower on all four datasets (e.g. -2.10 nats on Caltech)Helped
path-gradient estimator replacing total derivative for CNF trainingEquivariant CNF for phi^4 lattice field theory · Lattice 12x12 to 32x32reverse KL divergence: lower at all sizes (e.g. .1833 to .0498 at 32x32)Helped
path-gradient estimator per-iteration runtime overhead over total derivativeEquivariant CNF for phi^4 lattice field theory · Lattice 12x12, 20x20, 32x32time per iteration increase: +14%, +12%, +5% per iterationHurt
path-gradient estimator replacing total derivative for FFJORD VAE, test NLLFFJORD VAE · MNIST, Omniglot, Caltech, Frey Facestest negative log likelihood: MNIST 79.87 vs 80.10 (path better on MNIST, Omniglot, Caltech); Frey Faces 4.28 vs 4.36Mixed
path-gradient estimator in VAE training: wall-time per iterationFFJORD VAE · MNIST, Omniglotruntime relative to total estimator: up to 111% (Omniglot) and 98% (MNIST) of standard estimator runtimeHurt
path-gradient estimator on number of function evaluationsFFJORD VAE · Frey FacesNFENo clear effect

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