Delafo: An efficient portfolio optimization using deep neural networks
Portfolio optimization has been broadly investigated during the last decades and had a lot of
applications in finance and economics. In this paper, we study the portfolio optimization …
applications in finance and economics. In this paper, we study the portfolio optimization …
S2CFT: A new approach for paper submission recommendation
D Nguyen, S Huynh, P Huynh, CV Dinh… - SOFSEM 2021: Theory …, 2021 - Springer
There have been a massive number of conferences and journals in computer science that
create a lot of difficulties for scientists, especially for early-stage researchers, to find the most …
create a lot of difficulties for scientists, especially for early-stage researchers, to find the most …
A deep learning approach for solving Poisson's equations
Partial differential equations (PDEs) have a lot of applications in different fields of research
during the last decades. In this paper, we study a mesh-free deep learning method for …
during the last decades. In this paper, we study a mesh-free deep learning method for …
Solve systems of ordinary differential equations using deep neural networks
The systems of ordinary differential equations have been ubiquitously investigated and had
many applications for various areas in real life. This paper investigates a deep learning …
many applications for various areas in real life. This paper investigates a deep learning …
Iteratively-Linearized Set-Based Parameter Estimation for Uncertain Nonlinear Systems
Y Ito - IFAC-PapersOnLine, 2023 - Elsevier
This study presents a set-based method to estimate unknown parameters of continuous-time
structured nonlinear systems under uncertainties. Sets containing the parameters are found …
structured nonlinear systems under uncertainties. Sets containing the parameters are found …