arXiv research

New method estimates velocity fields for minimizing $f$-divergences without overfitting.

A neural network models pressure-Hessian from local velocity gradients in turbulent flows.

A conservative drifting method improves generative modeling by using KDE gradients, proving convergence rates.

New sampling method uses gradient-free IPS with RKHS velocity field.

NGBoost boosts multivariate probabilistic regression.

MeanFlow training is unstable due to misusing conditional velocity, leading to variance issues.

The paper explores rectified flows and their relation to optimal transport.

GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.

Proposes a method combining CNFs and rejection-resampling for sampling from unnormalized densities.

Study of $k$-almost Yamabe solitons in perfect fluid spacetimes.

We analyze a gradient flow of closed planar curves minimizing the anisoperimetric ratio. For such a flow the normal velocity is a function of the anisotropic curvature and it also depends on the total interfacial energy and enclosed area of the curve. In contrast to the gradient flow for the isoperimetric ratio, we sho…

The paper accelerates ISTA and FISTA algorithms for composite optimization problems.

We discuss some aspects about the computation of kinematic, spectroscopic, Fermi and astrometric relative velocities that are geometrically defined in general relativity. Mainly, we state that kinematic and spectroscopic relative velocities only depend on the 4-velocities of the observer and the test particle, unlike F…

DSNE visualizes data velocity in lower dimensions.

AB dynamically scales gradients to mitigate asynchronous training delays.

A scalable algorithm for sampling and fine-tuning models using Tilt Matching.

RFM improves CNFs by adding a boundary constraint term and matching velocity fields.

Log-ergodic model improves velocity of money prediction.

In this note we first set up an analogy between spin and vorticity of a perfect 2d-fluid flow, based on the Borel-Weil contruction of the irreducible unitary representations of SU(2), and looking at the Madelung-Bohm velocity attached to the ensuing spin wave functions. We also show that, in the framework of finite dim…

This work addresses unstable MeanFlow training by optimizing a coefficient in the loss function.

Optimal self-distillation improves generative models' velocity risk and mode recovery.

We prove that, in a space-time of dimension n>3 with a velocity field that is shear-free, vorticity-free and acceleration-free, the covariant divergence of the Weyl tensor is zero if the contraction of the Weyl tensor with the velocity is zero. The other way, if the covariant divergence of the Weyl tensor is zero, then…

Paper presents a new method to learn unnormalized models efficiently.

Proves strong solutions for graphical Brakke flows with $L^2$ normal velocity.

CCVFM uses coreset to improve generative models by refining residual flows.

SAGE generates subsurface velocity models from sparse well logs and seismic images.

New method distinguishes cause from effect using causal velocity.

EikoNet uses deep learning to solve the Eikonal equation quickly and efficiently.

The study examines perfect fluid spacetimes and their properties.

Anomalous diffusion in SGD reveals interactions between hyperparameters and Hessian.

LFIS uses a time-dependent velocity field to sample from complex distributions.

NN-Turb generates turbulent velocity statistics using neural networks.

Bitcoin's monetary velocity is constrained by network friction, leading to significant utility contraction during shocks.

The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is govern…

A new method interprets astrophysical spectra using geometric paths to distinguish line profiles.

Neural solver computes Wasserstein geodesics and velocity fields efficiently.

Develops scalable model for learning velocity fields in complex traffic scenarios.

Generative sampler learns velocity fields for efficient posterior inference.

Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Hamiltonian system. However, HMC is sensitive to large time discretizations and performs poorly if there is a mismatch between the spatial geo…

An $(r,n)$-velocity is an $r$-jet with source at $0 \in \R^n$, and target in a manifold $Y$. An $(r,n)$-velocity is said to be regular, if it has a representative which is an immersion at $0 \in \R^{n}$. The manifold $T^{r}_{n}Y$ of $(r,n)$-velocities as well as its open, $L^{r}_{n}$-invariant, dense submanifold $\Imm …

Time dilation $\frac{1}{\sqrt{1-v^2}}$ and relative velocity $v$ are observationally indistinguishable in the special theory of relativity, a duality that carries over into the general theory under Fermi coordinates along a curve (in coordinate-independent language, in the tangent Minkowski space along the curve). For …

New gradient flows improve high-dimensional sampling.

REGS samples from unnormalized distributions using gradient flow and neural networks.

We obtain expressions for the shear and the vorticity tensors of perfect-fluid spacetimes, in terms of the divergence of the Weyl tensor. For such spacetimes, we prove that if the gradient of the energy density is parallel to the velocity, then either the expansion rate is zero, or the vorticity vanishes. This statemen…

Study uses neural networks to predict wall quantities in turbulent flows.

The paper develops a new class of financial market models. These models are based on generalized telegraph processes: Markov random flows with alternating velocities and jumps occurring when the velocities are switching. While such markets may admit an arbitrage opportunity, the model under consideration is arbitrage-f…

In this study, the concept of dual Lorentzian homotetic exponential motions in is discussed and their velocities, accelerations obtained. Also, some geometric results between velocity and acceleration vectors of a point in a spatial motion are obtained. Finally, the theorems related to acceleration and acceleration cen…

HF-opt uses Hamiltonian dynamics to optimize functions, achieving accelerated rates with randomized integration time.