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cs.LG2025
Dynamic Rank Reinforcement Learning for Adaptive Low-Rank Multi-Head Self Attention in Large Language Models
Caner Erden
Dynamic Rank Reinforcement Learning (DR-RL) approximations rely on static rank assumptions, limiting their flexibility across diverse linguistic contexts. Our method dynamically mo…
cs.LG2025
Q-Sat AI: Machine Learning-Based Decision Support for Data Saturation in Qualitative Studies
Hasan Tutar, Caner Erden, Ümit Şentürk
The determination of sample size in qualitative research has traditionally relied on the subjective and often ambiguous principle of data saturation, which can lead to inconsistenc…
cs.LG2023★ 8 cited
Enhancing Machine Learning Model Performance with Hyper Parameter Optimization: A Comparative Study
Caner Erden, Halil Ibrahim Demir, Abdullah Hulusi Kökçam
One of the most critical issues in machine learning is the selection of appropriate hyper parameters for training models. Machine learning models may be able to reach the best trai…