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Found 6 papers, 0 papers with code
Understanding the Generalization Benefits of Late Learning Rate Decay
Why do neural networks trained with large learning rates for a longer time often lead to better generalization?
Statistical Spatially Inhomogeneous Diffusion Inference
Inferring a diffusion equation from discretely-observed measurements is a statistical challenge of significant importance in a variety of fields, from single-molecule tracking in biophysical systems to modeling financial instruments.
Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications.
Infinite forecast combinations based on Dirichlet process
Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series.
High-dimensional density estimation with tensorizing flow
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data.
A semigroup method for high dimensional committor functions based on neural network
This paper proposes a new method based on neural networks for computing the high-dimensional committor functions that satisfy Fokker-Planck equations.
