mlsanityData to 7 Oct 2026

Complex normalizing flows can almost be information K"ahler-Ricci flows

arXiv 2604.17954 ↗First posted 20 Apr 2026math.DG cs.LGcited by 0

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Abstract (arXiv)

We develop interconnections between the complex normalizing flow for data drawn from Borel probability measures on the twofold realification of the complex manifold and a nonlinear flow nearly K"ahler-Ricci. The complex normalizing flow relates the initial and target realified densities under the complex change of variables, necessitating the log determinant of the ensemble of Wirtinger Jacobians. The Ricci curvature of a K"ahler 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 a Ricci curvature term under differentiation and conditions. The log density under the normalizing flow is kindred to a spatial information metric under an augmented Jacobian and a Bayesian perspective to the parameter, thus under the continuum limit the log likelihood matches a Fisher metric, or more closely a K"ahler cross-entropy Hessian. This recovers a K"ahler-Ricci flow variation up to a time derivative and expectation, or an average-valued K"ahler-Einstein flow. 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 our derived K"ahler flow.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 57 references that are in the map. First posted on arXiv 20 Apr 2026.

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References · 21

  1. 2018Neural Ordinary Differential Equationscited by 3,733
  2. 2015Variational Inference with Normalizing Flowscited by 2,161
  3. 2016Density estimation using Real NVPcited by 2,029
  4. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  6. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 584
  7. 2020Flows for simultaneous manifold learning and density estimationcited by 117
  8. 2021Diffusion Normalizing Flowcited by 77
  9. 2019Discrete Flows: Invertible Generative Models of Discrete Datacited by 68
  10. 2018Conditional Density Estimation with Bayesian Normalising Flowscited by 54
  11. 2021Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equationscited by 38
  12. 2018Monge-Amp`ere Flow for Generative Modelingcited by 33
9 more
  1. 2021Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flowscited by 30
  2. 2020Transforming Gaussian Processes With Normalizing Flowscited by 28
  3. 2022Bridging Mean-Field Games and Normalizing Flows with Trajectory Regularizationcited by 17
  4. 2020Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamicscited by 14
  5. 2018Deep Diffeomorphic Normalizing Flowscited by 10
  6. 2024Quantum Normalizing Flows for Anomaly Detectioncited by 5
  7. 2023Normalizing flows as approximations of optimal transport maps via linear-control neural ODEscited by 2
  8. 2017The nonparametric Fisher geometry and the chi-square process density priorcited by 1
  9. 2025Flatness After All?cited by 1

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