18 citations · 22 across the 11 of their papers we have counts for
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stat.ML2025
Gibbs randomness-compression proposition
M Süzen
A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a lossy compression process. In building thi…
stat.ML2019★ 3 cited
Generalised learning of time-series: Ornstein-Uhlenbeck processes
Mehmet Süzen, Alper Yegenoglu
In machine learning, statistics, econometrics and statistical physics, cross-validation (CV) is used asa standard approach in quantifying the generalisation performance of a statis…
stat.ML2017★ 1 cited
Spectral Ergodicity in Deep Learning Architectures via Surrogate Random Matrices
Mehmet Süzen, Cornelius Weber, Joan J. Cerdà
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 (…