Complex normalizing flows can be information Kähler-Ricci flows
- URL: http://arxiv.org/abs/2604.17954v1
- Date: Mon, 20 Apr 2026 08:36:35 GMT
- Title: Complex normalizing flows can be information Kähler-Ricci flows
- Abstract summary: Complex normalizing flow relates the initial and target realified densities under the complex change of variables.<n>The Ricci curvature of a Khler manifold is the second order mixed Wirtinger partial derivative of the log of the local density of the volume form.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We develop interconnections between the complex normalizing flow for data drawn from Borel probability measures on the twofold realification of the complex manifold and the Kähler-Ricci flow. The complex normalizing flow relates the initial and target realified densities under the complex change of variables, necessitating the log determinant of the Wirtinger Jacobian. The Ricci curvature of a Kähler manifold is the second order mixed Wirtinger partial derivative of the log of the local density of the volume form. Therefore, we reconcile these two facts by drawing forth the connection that the log determinant used in the complex normalizing flow matches the Ricci curvature term under differentiation and conditions. The log density under the normalizing flow is kindred to a spatial Fisher information metric under a holomorphic pullback and a Bayesian perspective to the parameter, thus under the continuum limit the log likelihood matches a Fisher metric, recovering the Kähler-Ricci flow up to expectation. Using this framework, we establish other relevant results, attempting to bridge the statistical and ordinary behaviors of the complex normalizing flow to the geometric features of the Kähler-Ricci flow.
Related papers
- Simplifying Flow Matching Transformations with Low-Rank Mixture Models [58.797704398690165]
We propose using mixtures of probabilistic principal component analyzers (MPPCA) as the latent density for normalizing flows.<n>We simplify the learned flow transformation by learning a latent distribution that more closely aligns with the data distribution in terms of KL divergence.<n>MPPCA models can be fit quickly and cheaply using the expectation-maximization algorithm, making them a practical choice for initializing latent distributions even in high-dimensional generative tasks.
arXiv Detail & Related papers (2026-06-29T03:03:06Z) - Topological Flow Matching [56.16372813551555]
Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance.<n>We introduce topological flow matching, a topology-aware generalization of flow matching.<n>We demonstrate its effectiveness on diverse structured datasets, including brain fMRIs, ocean currents, seismic events, and traffic flows.
arXiv Detail & Related papers (2026-06-14T16:29:33Z) - An Explicit Surrogate for Gaussian Mixture Flow Matching with Wasserstein Gap Bounds [0.17478203318226307]
We study training-free flow matching between two Gaussian mixture models.<n>We use explicit velocity fields that transport one mixture into the other over time.
arXiv Detail & Related papers (2026-03-30T20:47:22Z) - Radon--Wasserstein Gradient Flows for Interacting-Particle Sampling in High Dimensions [0.9940728137241214]
gradient flows of the Kullback--Leibler divergence evolve a distribution toward a target density known only up to a normalizing constant.<n>We introduce new gradient flows of the KL divergence with a remarkable combination of properties.<n>They admit accurate interacting-particle approximations in high dimensions, and the per-step cost scales linearly in both the number of particles and the dimension.
arXiv Detail & Related papers (2026-02-05T02:38:56Z) - Pathway to $O(\sqrt{d})$ Complexity bound under Wasserstein metric of flow-based models [1.724966705006084]
We provide tools to estimate the error of flow-based generative models under the Wasserstein metric.<n>We show this error can be explicitly controlled by two parts: the Lipschitzness of the push-forward maps of the backward flow which scales independently of the dimension.
arXiv Detail & Related papers (2025-12-07T07:26:39Z) - Evolution of Gaussians in the Hellinger-Kantorovich-Boltzmann gradient flow [44.500314182711236]
We derive explicit ordinary equations govern the evolution of mean, covariance, and mass under HK-Boltzmann flow.<n>We demonstrate exponential convergence to equilibrium through constructing a gradient structure associated with a Polyak-Lojasiewicz-type inequalities.<n>We extend our analysis to non-Gaussian targets exhibiting strong log-lambda-concavity.
arXiv Detail & Related papers (2025-04-29T03:54:56Z) - Gaussian Interpolation Flows [11.340847429991525]
This work investigates the well-posedness of simulation-free continuous normalizing flows built on Gaussian denoising.
We establish the Lipschitz regularity of the flow velocity field, the existence and uniqueness of the flow, and the continuity of the flow map.
We also study the stability of these flows in source distributions and perturbations of the velocity field, using the quadratic Wasserstein distance as a metric.
arXiv Detail & Related papers (2023-11-20T00:59:20Z) - Normalizing flows for lattice gauge theory in arbitrary space-time
dimension [135.04925500053622]
Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions.
We discuss masked autoregressive with tractable and unbiased Jacobian determinants, a key ingredient for scalable and exact flow-based sampling algorithms.
For concreteness, results from a proof-of-principle application to SU(3) gauge theory in four space-time dimensions are reported.
arXiv Detail & Related papers (2023-05-03T19:54:04Z) - Convolutional Filtering on Sampled Manifolds [122.06927400759021]
We show that convolutional filtering on a sampled manifold converges to continuous manifold filtering.
Our findings are further demonstrated empirically on a problem of navigation control.
arXiv Detail & Related papers (2022-11-20T19:09:50Z) - Joint Manifold Learning and Density Estimation Using Normalizing Flows [4.939777212813711]
We introduce two approaches, namely per-pixel penalized log-likelihood and hierarchical training, to answer the question.
We propose a single-step method for joint manifold learning and density estimation by disentangling the transformed space.
Results validate the superiority of the proposed methods in simultaneous manifold learning and density estimation.
arXiv Detail & Related papers (2022-06-07T13:35:14Z) - Nonlinear Isometric Manifold Learning for Injective Normalizing Flows [58.720142291102135]
We use isometries to separate manifold learning and density estimation.
We also employ autoencoders to design embeddings with explicit inverses that do not distort the probability distribution.
arXiv Detail & Related papers (2022-03-08T08:57:43Z) - Efficient CDF Approximations for Normalizing Flows [64.60846767084877]
We build upon the diffeomorphic properties of normalizing flows to estimate the cumulative distribution function (CDF) over a closed region.
Our experiments on popular flow architectures and UCI datasets show a marked improvement in sample efficiency as compared to traditional estimators.
arXiv Detail & Related papers (2022-02-23T06:11:49Z) - Assignment Flows for Data Labeling on Graphs: Convergence and Stability [69.68068088508505]
This paper establishes conditions on the weight parameters that guarantee convergence of the continuous-time assignment flow to integral assignments (labelings)
Several counter-examples illustrate that violating the conditions may entail unfavorable behavior of the assignment flow regarding contextual data classification.
arXiv Detail & Related papers (2020-02-26T15:45:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.