178 citations · 234 across the 4 of their papers we have counts for
4 papers
A new architecture of high-order deep neural networks that learn martingales
Syoiti Ninomiya, Yuming Ma
A new deep-learning neural network architecture based on high-order weak approximation algorithms for stochastic differential equations (SDEs) is proposed. The architecture enables…
A high-order recombination algorithm for weak approximation of stochastic differential equations
Syoiti Ninomiya, Yuji Shinozaki
This paper presents an algorithm for applying the high-order recombination method, originally introduced by Lyons and Litterer in ``High-order recombination and an application to c…
A new weak approximation scheme of stochastic differential equations and the Runge-Kutta method
Mariko Ninomiya, Syoiti Ninomiya
In this paper, authors successfully construct a new algorithm for the new higher order scheme of weak approximation of SDEs. The algorithm presented here is based on [1][2]. Althou…
Weak approximation of stochastic differential equations and application to derivative pricing
Syoiti Ninomiya, Nicolas Victoir
The authors present a new simple algorithm to approximate weakly stochastic differential equations in the spirit of [1] and [2]. They apply it to the problem of pricing Asian optio…