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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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4238451,2681,690 · Jun 202019922001200920172026
48 results for flow map learning

sFML learns stochastic dynamical systems from data.

problem Learning unknown stochastic dynamical systems from measurement data.
method sFML extends FML for deterministic systems, using a stochastic flow map composed of deterministic and stochastic sub-maps.
result sFML constructs a stochastic evolution model approximating unknown stochastic systems.

VFMs use noise adapters to conditionally generate images in one step.

problem Conditional image generation with iterative models is slow and requires explicit sampling paths.
method Developed a variational flow map framework that learns noise distributions for conditional sampling.
result VFMs achieve well-calibrated conditional samples in a single forward pass.

In this paper, we investigate the Gauss maps of a Ricci-mean curvature flow. A Ricci-mean curvature flow is a coupled equation of a mean curvature flow and a Ricci flow on the ambient manifold. Ruh and Vilms proved that the Gauss map of a minimal submanifold in a Euclidean space is a harmonic map, and Wang extended thi…

2017-02-15abs ↗pdf ↗

Study heat flow for half-harmonic maps and harmonic maps with free boundary.

problem Integrability and regularity of half-harmonic maps and harmonic maps with free boundary.
method Introduced a heat flow associated to half-harmonic maps and constructed weak solutions via Ginzburg-Landau approximation.
result Proved partial regularity of weak solutions in space and time.

Riemannian stochastic gradient descent approximates a diffusion process called Riemannian stochastic modified flow.

problem Improving convergence rate of Riemannian stochastic gradient descent.
method Using stochastic differential geometry, the paper shows RSGD can be approximated by the Riemannian stochastic modified flow (RSMF).
result RSGD can be approximated by the solution to the RSMF driven by an infinite-dimensional Wiener process, increasing the order of approximation.

Normalizing flows optimize Jacobian determinant for unique likelihood objective.

problem Optimizing normalizing flows for unique likelihood.
method Showed Jacobian determinant is unique for given distributions, leading to a unique global optimum. Used eigenvalues of auto-correlation matrix for explicit likelihood expression.
result Explicit expression of likelihood for flows, independent of neural network parameterization, with theoretical optimal value.

In this paper we study the Teichmüller harmonic map flow as introduced by Rupflin and Topping [15]. It evolves pairs of maps and metrics (u,g)(u,g) into branched minimal immersions, or equivalently into weakly conformal harmonic maps, where uu maps from a fixed closed surface MM with metric gg to a general target manif…

2017-11-24abs ↗pdf ↗

Develops a new method for learning discrete distributions without embedding them in a continuous space.

problem Challenges in learning discrete distributions using current methodologies.
method Introduces a MAD invertible map and a mixed variational flow (MAD Mix) for discrete distributions.
result MAD Mix produces more reliable approximations than continuous-embedding flows.

New approach to heat flow for half-harmonic maps, related to minimal surfaces.

problem Heat flow for half-harmonic maps from S1S^1 to closed target manifolds.
method Classical approach using Dirichlet-to-Neumann operator for the Laplace equation.
result Analogous results to 1985 harmonic map heat flow, valid for finite-energy data.

New method improves generative modeling on convex domains using regularized mirror maps and Student-t priors.

problem Challenges in generative modeling on convex domains with heavy-tailed targets.
method Mirror Flow Matching with regularized mirror maps and Student-t priors.
result Empirically outperforms baselines and achieves competitive sample quality.

Study on Gaussian interpolation flows for generative modeling.

problem Theoretical properties and regularizing effect of Gaussian denoising in continuous normalizing flows.
method Unified framework of Gaussian interpolation flow, Lipschitz regularity, existence and uniqueness of flow, stability analysis.
result Established theoretical properties of Gaussian interpolation flows, including Lipschitz continuity and existence of flow.

We introduce a flow of maps from a compact surface of arbitrary genus to an arbitrary Riemannian manifold which has elements in common with both the harmonic map flow and the mean curvature flow, but is more effective at finding minimal surfaces. In the genus 0 case, our flow is just the harmonic map flow, and it tries…

2012-05-29abs ↗pdf ↗

Paper introduces a novel map learning algorithm for domain translation and adaptation.

problem Learning a map between related data spaces that can be applied to out-of-sample data and satisfies application-specific constraints.
method Utilizes normalizing flows to parameterize a map that minimizes a probability distance and application-specific regularizers, solving a modified optimal transport problem.
result The proposed method (parOT) outperforms existing optimal transport approaches in domain adaptation and translation tasks.

Study Liouville theorems for harmonic maps along ancient super Ricci flows.

problem Proving Liouville theorems for harmonic maps under specific geometric conditions.
method Using Perelman's reduced geometric viewpoint, derive Liouville theorems with controlled growth.
result Sharp growth conditions and new Liouville theorems for both non-positively and positively curved target spaces.

A new framework solves complex optimization problems with continuous worst-case distributions.

problem Optimizing under uncertain distributions with continuous worst-case scenarios.
method Flow-based distributionally robust optimization (DRO) with Wasserstein uncertainty sets and invertible transport maps.
result The framework finds continuous worst-case distributions and samples efficiently.

New framework formalizes estimating valid transport maps, revealing their statistical limits.

problem Estimating valid transport maps in generative modeling.
method Formalized a minimax framework for estimating valid transport maps.
result Estimating any valid transport map is as hard as estimating the optimal transport map under standard stability assumptions.

The paper studies harmonic map heat flow stability and decay rates.

problem Analyzing stability and decay rates of harmonic map heat flow solutions.
method Use of homogeneous Besov space B˙p,dp(Rd)\dot{B}^{\frac{d}{p}}_{p,\infty}(\mathbb{R}^d) for small initial data and self-similar decay assumption.
result Decay rates for solutions of the harmonic map flow of the form ablau(t)L(Rd)Ct12\| abla u(t) \|_{L^\infty(\mathbb{R}^d)}\leq Ct^{-\frac12} and self-similar decay under stronger initial conditions.

This study compares two methods for sampling with transport maps, finding flow-based proposals work better for multimodal distributions.

problem Sampling from distributions with complex geometries.
method Compares two approaches: (i) proposal draws from the flow and (ii) reparametrization.
result Flow-based proposals are more effective for multimodal distributions in high dimensions, while reparametrization methods are more robust in other scenarios.

Study mean curvature flow into evolving manifold with coupled flows.

problem Analyzing mean curvature flow in evolving Riemannian manifolds.
method Coupling Ricci flow and harmonic map heat flow, calculating variations, and using Harnack expressions.
result Obtained a Huisken monotonicity-type formula for mean curvature flow.