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Found 7 papers, 5 papers with code
Convergence Analysis of Probability Flow ODE for Score-based Generative Models
Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions.
An operator learning perspective on parameter-to-observable maps
Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering.
Sampling via Gradient Flows in the Space of Probability Measures
Our third contribution is to study, and develop efficient algorithms based on Gaussian approximations of the gradient flows; this leads to an alternative to particle methods.
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
The flow in the Gaussian space may be understood as a Gaussian approximation of the flow.
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
The equation residuals are used to inform the spline learning in a Bayesian manner, where approximate Bayesian uncertainty calibration techniques are employed to approximate posterior distributions of the trainable parameters.
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
The resulting geo-FNO model has both the computation efficiency of FFT and the flexibility of handling arbitrary geometries.
Long Random Matrices and Tensor Unfolding
In this paper, we consider the singular values and singular vectors of low rank perturbations of large rectangular random matrices, in the regime the matrix is "long": we allow the number of rows (columns) to grow polynomially in the number of columns (rows).
