1 citations · 1 across the 4 of their papers we have counts for
5 papers
Error of randomized Milstein scheme for scalar SDEs with noisy information about coefficients and Wiener process
Paweł M. Morkisz, Paweł Przybyłowicz, Martyna Wiącek
We investigate the strong approximation of scalar stochastic differential equations when the available standard information about the drift coefficient, the diffusion coefficient,…
Neural Network-Based Estimation of Time-Dependent Parameters in AR(p) Processes
Agnieszka Kopeć, Paweł Przybyłowicz, Martyna Wiącek
We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep lear…
On the error of the Euler scheme for approximation of solutions of nonlinear DDEs under inexact information
Paweł Przybyłowicz, Martyna Wiącek
We analyze the behavior of the Euler method for delay differential equations under nonstandard assumptions on the right-hand-side function f, when evaluations of f are corrupted by…
NLPre: a revised approach towards language-centric benchmarking of Natural Language Preprocessing systems
Martyna Wiącek, Piotr Rybak, Łukasz Pszenny +1
With the advancements of transformer-based architectures, we observe the rise of natural language preprocessing (NLPre) tools capable of solving preliminary NLP tasks (e.g. tokenis…
Deep learning-based estimation of time-dependent parameters in Markov models with application to nonlinear regression and SDEs
Andrzej Kałuża, Paweł M. Morkisz, Bartłomiej Mulewicz +2
We present a novel deep learning method for estimating time-dependent parameters in Markov processes through discrete sampling. Departing from conventional machine learning, our ap…