Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient
adaptive algorithms for neural networks

Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks

28 May 2021

Dong-Young Lim

Sotirios Sabanis

Papers citing "Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks"

6 / 6 papers shown

Title
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients Stefano Bruno Sotirios Sabanis DiffM 50 0 0 06 May 2025
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data YongKyung Oh Dongyoung Lim Sungil Kim AI4TS 43 11 0 22 Feb 2024
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates Stefano Bruno Ying Zhang Dong-Young Lim Ömer Deniz Akyildiz Sotirios Sabanis DiffM 33 4 0 22 Nov 2023
Global convergence of optimized adaptive importance samplers Ömer Deniz Akyildiz 25 7 0 02 Jan 2022
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions Wei Deng Guang Lin F. Liang BDL 42 27 0 19 Oct 2020
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling Difan Zou Pan Xu Quanquan Gu 38 35 0 19 Oct 2020