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20152026
most citedA Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions

34 citations · 212 across the 40 of their papers we have counts for

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Showing 2018Show all

6 papers · 1 filter

physics.chem-ph2018★ 5 cited

Continuum limit and preconditioned Langevin sampling of the path integral molecular dynamics

Jianfeng Lu, Yulong Lu, Zhennan Zhou

We investigate the continuum limit that the number of beads goes to infinity in the ring polymer representation of thermal averages. Studying the continuum limit of the trajectory…

math.FA2018★ 6 cited

Gradient flow structure and exponential decay of the sandwiched Rényi divergence for primitive Lindblad equations with GNS-detailed balance

Yu Cao, Jianfeng Lu, Yulong Lu

We study the entropy production of the sandwiched Rényi divergence under the primitive Lindblad equation with GNS-detailed balance. We prove that the Lindblad equation can be ident…

math.PR2018

On the rate of convergence of empirical measure in Wasserstein distance for unbounded density function

Anning Liu, Jian-Guo Liu, Yulong Lu

We consider a sequence of identically independently distributed random samples from an absolutely continuous probability measure in one dimension with unbounded density. We establi…

math.AP2018★ 1 cited

An operator splitting scheme for the fractional kinetic Fokker-Planck equation

Manh Hong Duong, Yulong Lu

In this paper, we develop an operator splitting scheme for the fractional kinetic Fokker-Planck equation (FKFPE). The scheme consists of two phases: a fractional diffusion phase an…

math.AP2018★ 17 cited

Exponential decay of Rényi divergence under Fokker-Planck equations

Yu Cao, Jianfeng Lu, Yulong Lu

We prove the exponential convergence to the equilibrium, quantified by Rényi divergence, of the solution of the Fokker-Planck equation with drift given by the gradient of a strictl…

math.AP2018★ 6 cited

Scaling limit of the Stein variational gradient descent: the mean field regime

Jianfeng Lu, Yulong Lu, James Nolen

We study an interacting particle system in motivated by Stein variational gradient descent [Q. Liu and D. Wang, NIPS 2016], a deterministic algorithm for sampling fr…