8 citations · 9 across the 5 of their papers we have counts for
6 papers
Multiscale Aggregated Hierarchical Attention (MAHA): A Game Theoretic and Optimization Driven Approach to Efficient Contextual Modeling in Large Language Models
Caner Erden
The quadratic computational complexity of MultiHead SelfAttention (MHSA) remains a fundamental bottleneck in scaling Large Language Models (LLMs) for longcontext tasks. While spars…
Predicting California Bearing Ratio with Ensemble and Neural Network Models: A Case Study from Turkiye
Abdullah Hulusi Kökçam, Uğur Dağdeviren, Talas Fikret Kurnaz +2
The California Bearing Ratio (CBR) is a key geotechnical indicator used to assess the load-bearing capacity of subgrade soils, especially in transportation infrastructure and found…
Soil Compaction Parameters Prediction Based on Automated Machine Learning Approach
Caner Erden, Alparslan Serhat Demir, Abdullah Hulusi Kokcam +2
Soil compaction is critical in construction engineering to ensure the stability of structures like road embankments and earth dams. Traditional methods for determining optimum mois…
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…
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…
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…