The Path-Dependent Neural Jump ODE (PD-NJ-ODE) is a model for predicting...
This paper studies the problem of forecasting general stochastic process...
This paper presents new machine learning approaches to approximate the
s...
Continuous stochastic processes are widely used to model time series tha...
We introduce Denise, a deep learning based algorithm for decomposing pos...
We estimate the Lipschitz constants of the gradient of a deep neural net...