Deep neural networks for faster nonparametric regression

Date

2021-07-19

Author

Kaygusuz, Mehmet Ali
Purutçuoğlu Gazi, Vilda

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

206
views

0
downloads

URI

https://hdl.handle.net/11511/100871

Conference Name

10th World Congress in Probability and Statistics

Collections

Department of Statistics, Conference / Seminar

Suggestions

OpenMETU
Core

Deep Learning Techniques and Optimization Strategies in Big Data Analytics Thomas ,, J. Joshua; Karagöz, Pınar; Ahmad, Bazeer; Vasant, Pandian (IGI Global, 2020-01-01)
Deep Learning with Multivariate Adaptive Regression Spline with Bagging Methods Kaygusuz, Mehmet Ali; Somuncuoğlu, Abdullah Nuri; Purutçuoğlu Gazi, Vilda (2021-09-01)
Deep Learning Based Speed Up of Fluid Dynamics Solvers Acar, Deniz Alper; Uzol, Oğuz; Department of Aerospace Engineering (2022-9-8) In this thesis, two distinct deep learning-based methods for the speed-up of fluid dynamics solvers are proposed. The first method called Parametric Encoded Physics informed neural network (PEPINN), is utilized to solve transient fluid dynamics problems. PEPINN is an alternative to the Physics informed neural networks (PINN) and is based on the parametric encoding of the problem domain. In PEPINN the automatic differentiation for calculation of the problem residual is replaced with finite difference kernel...
Artificial neural networks for transfer aligment and calibration of inertial navigation systems Tekinalp, Ozan (2001-08-09)
Structured neural networks for modeling and identification of nonlinear mechanical systems Kılıç, Ergin; Dölen, Melik; Koku, Ahmet Buğra; Department of Mechanical Engineering (2012) Most engineering systems are highly nonlinear in nature and thus one could not develop efficient mathematical models for these systems. Artificial neural networks, which are used in estimation, filtering, identification and control in technical literature, are considered as universal modeling and functional approximation tools. Unfortunately, developing a well trained monolithic type neural network (with many free parameters/weights) is known to be a daunting task since the process of loading a specific pat...

Citation Formats

M. A. Kaygusuz and V. Purutçuoğlu Gazi, “Deep neural networks for faster nonparametric regression,” presented at the 10th World Congress in Probability and Statistics, Seoul, Güney Kore, 2021, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100871.