1 code implementation • 14 Jun 2020 • Mehmet Süzen
A numerical approach is developed for detecting the equivalence of deep learning architectures.
1 code implementation • 10 Nov 2019 • Mehmet Süzen, J. J. Cerdà, Cornelius Weber
Establishing associations between the structure and the generalisation ability of deep neural networks (DNNs) is a challenging task in modern machine learning.
no code implementations • 21 Oct 2019 • Mehmet Süzen, Alper Yegenoglu
The performance of the primary models are evaluated simultaneously bycomputing the deviations from the originally removed data points and out-of-sample (OSS) data. Full cross-validation in time-series models can be practiced with rCV along with generating learning curves.
no code implementations • 16 Apr 2019 • Oguzhan Gencoglu, Mark van Gils, Esin Guldogan, Chamin Morikawa, Mehmet Süzen, Mathias Gruber, Jussi Leinonen, Heikki Huttunen
Recent advancements in machine learning research, i. e., deep learning, introduced methods that excel conventional algorithms as well as humans in several complex tasks, ranging from detection of objects in images and speech recognition to playing difficult strategic games.
1 code implementation • 25 Apr 2017 • Mehmet Süzen, Cornelius Weber, Joan J. Cerdà
It is observed that as the matrix size increases the level of spectral ergodicity of the ensemble rises, i. e., the eigenvalue spectra obtained for a single realisation at random from the ensemble is closer to the spectra obtained averaging over the whole ensemble.