36 citations · 307 across the 37 of their papers we have counts for
37 papers
Embedding stochastic differential equations into neural networks via dual processes
Naoki Sugishita, Jun Ohkubo
We propose a new approach to constructing a neural network for predicting expectations of stochastic differential equations. The proposed method does not need data sets of inputs a…
Statistics for stochastic differential equations and approximations of resolvent
Jun Ohkubo
The numerical evaluation of statistics plays a crucial role in statistical physics and its applied fields. It is possible to evaluate the statistics for a stochastic differential e…
Numerical methods to evaluate Koopman matrix from system equations
Jun Ohkubo
The Koopman operator is beneficial for analyzing nonlinear and stochastic dynamics; it is linear but infinite-dimensional, and it governs the evolution of observables. The extended…
Semi-supervised learning combining backpropagation and STDP: STDP enhances learning by backpropagation with a small amount of labeled data in a spiking neural network
Kotaro Furuya, Jun Ohkubo
A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learni…
Connection among stochastic Hamilton-Jacobi-Bellman equation, path-integral, and Koopman operator on nonlinear stochastic optimal control
Jun Ohkubo
The path-integral control, which stems from the stochastic Hamilton-Jacobi-Bellman equation, is one of the methods to control stochastic nonlinear systems. This paper gives a new i…
Combinatorics for calculating expectation values of functions in systems with evolution governed by stochastic differential equations
Jun Ohkubo
Stochastic differential equations are widely used in various fields; in particular, the usefulness of duality relations has been demonstrated in some models such as population mode…