4 papers
Imitation Learning for Autonomous Driving: Insights from Real-World Testing
Hidayet Ersin Dursun, Yusuf Güven, Tufan Kumbasar
This work focuses on the design of a deep learning-based autonomous driving system deployed and tested on the real-world MIT Racecar to assess its effectiveness in driving scenario…
Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy
Yusuf Guven, Tufan Kumbasar
Uncertainty Quantification (UQ) is crucial for deploying reliable Deep Learning (DL) models in high-stakes applications. Recently, General Type-2 Fuzzy Logic Systems (GT2-FLSs) hav…
FAME: Introducing Fuzzy Additive Models for Explainable AI
Omer Bahadir Gokmen, Yusuf Guven, Tufan Kumbasar
In this study, we introduce the Fuzzy Additive Model (FAM) and FAM with Explainability (FAME) as a solution for Explainable Artificial Intelligence (XAI). The family consists of th…
Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals
Ata Koklu, Yusuf Guven, Tufan Kumbasar
In this paper, we tackle the task of generating Prediction Intervals (PIs) in high-risk scenarios by proposing enhancements for learning Interval Type-2 (IT2) Fuzzy Logic Systems (…