Research map / Probabilistic and scientific modeling / Variational and generative sampling
Path-Gradient Estimators for Continuous Normalizing Flows
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| path-gradient estimator replacing total derivative for CNF training | Equivariant CNF for phi^4 lattice field theory · Lattice 12x12 to 32x32 | ESS: +1.65 (12x12), +6.1 (20x20), +22.3 (32x32) points | Helped |
| path-gradient estimator replacing total derivative for FFJORD VAE training | FFJORD VAE · MNIST, Omniglot, Caltech Silhouettes, Frey Faces | negative ELBO: lower on all four datasets (e.g. -2.10 nats on Caltech) | Helped |
| path-gradient estimator replacing total derivative for CNF training | Equivariant CNF for phi^4 lattice field theory · Lattice 12x12 to 32x32 | reverse KL divergence: lower at all sizes (e.g. .1833 to .0498 at 32x32) | Helped |
| path-gradient estimator per-iteration runtime overhead over total derivative | Equivariant CNF for phi^4 lattice field theory · Lattice 12x12, 20x20, 32x32 | time per iteration increase: +14%, +12%, +5% per iteration | Hurt |
| path-gradient estimator replacing total derivative for FFJORD VAE, test NLL | FFJORD VAE · MNIST, Omniglot, Caltech, Frey Faces | test negative log likelihood: MNIST 79.87 vs 80.10 (path better on MNIST, Omniglot, Caltech); Frey Faces 4.28 vs 4.36 | Mixed |
| path-gradient estimator in VAE training: wall-time per iteration | FFJORD VAE · MNIST, Omniglot | runtime relative to total estimator: up to 111% (Omniglot) and 98% (MNIST) of standard estimator runtime | Hurt |
| path-gradient estimator on number of function evaluations | FFJORD VAE · Frey Faces | NFE | No clear effect |
Closest papers by meaning
- Fast and unified path gradient estimators for normalizing flows (ICLR 2024 · similarity 0.92)
- Accelerating Continuous Normalizing Flow with Trajectory Polynomial Regularization (AAAI 2021 · similarity 0.87)
- Asymptotically exact variational flows via involutive MCMC kernels (NeurIPS 2025 · similarity 0.85)
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- An Error Analysis of Flow Matching for Deep Generative Modeling (ICML 2025 · similarity 0.84)
- Minimizing $f$-Divergences by Interpolating Velocity Fields (ICML 2024 · similarity 0.84)
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