Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Süzen, Mehmet"'
Autor:
Süzen, Mehmet
A pedagogical formulation of Loschmidt's paradox and H-theorem is presented with basic notation on occupancy on discrete states without invoking velocity collision operators. A conjecture, so called H-theorem do-conjecture, is formulated. Causal infe
Externí odkaz:
http://arxiv.org/abs/2310.01458
Autor:
Süzen, Mehmet
A numerical approach is developed for detecting the equivalence of deep learning architectures. The method is based on generating Mixed Matrix Ensembles (MMEs) out of deep neural network weight matrices and {\it conjugate circular ensemble} matching
Externí odkaz:
http://arxiv.org/abs/2006.13687
Establishing associations between the structure and the generalisation ability of deep neural networks (DNNs) is a challenging task in modern machine learning. Producing solutions to this challenge will bring progress both in the theoretical understa
Externí odkaz:
http://arxiv.org/abs/1911.07831
Autor:
Süzen, Mehmet, Yegenoglu, Alper
In machine learning, statistics, econometrics and statistical physics, cross-validation (CV) is used asa standard approach in quantifying the generalisation performance of a statistical model. A directapplication of CV in time-series leads to the los
Externí odkaz:
http://arxiv.org/abs/1910.09394
Autor:
Gencoglu, Oguzhan, van Gils, Mark, Guldogan, Esin, Morikawa, Chamin, Süzen, Mehmet, Gruber, Mathias, Leinonen, Jussi, Huttunen, Heikki
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 dif
Externí odkaz:
http://arxiv.org/abs/1904.07633
Autor:
Süzen, Mehmet
Algorithms for rare event complex systems simulations are proposed. Compressed Sensing (CS) has {\it revolutionized} our understanding of limits in signal recovery and has forced us to re-define Shannon-Nyquist sampling theorem for sparse recovery. A
Externí odkaz:
http://arxiv.org/abs/1804.09781
In this work a novel method to quantify spectral ergodicity for random matrices is presented. The new methodology combines approaches rooted in the metrics of Thirumalai-Mountain (TM) and Kullbach-Leibler (KL) divergence. The method is applied to a g
Externí odkaz:
http://arxiv.org/abs/1704.08303
Autor:
Süzen, Mehmet
I have shown conceptually that quantum state has a direct relationship to gravitational constant due to entropic force posed by Verlinde's argument and part of the Newton-Schr\"odinger equation (N-S) in the context of gravity induced collapse of the
Externí odkaz:
http://arxiv.org/abs/1612.00288
Autor:
Süzen, Mehmet, Ajraou, Abed
A practical approach to evaluate performance of a Gaussian process regression models (GPR) for irregularly sampled sparse time-series is introduced. The approach entails construction of a secondary autoregressive model using the fine scale prediction
Externí odkaz:
http://arxiv.org/abs/1611.02978
Autor:
Süzen, Mehmet
Power-law exponents in the convergence to effective ergodicity is quantified for Ising-Lenz model in one dimension. Modified Thirumalai-Mountain (TM) metric for magnetisation is computed for the range of temperature values under strongly correlated d
Externí odkaz:
http://arxiv.org/abs/1606.08693