2 citations · 5 across the 13 of their papers we have counts for
5 papers · 1 filter
Logit-Level Uncertainty Quantification in Vision-Language Models for Histopathology Image Analysis
Betul Yurdem, Ferhat Ozgur Catak, Murat Kuzlu +1
Vision-Language Models (VLMs) with their multimodal capabilities have demonstrated remarkable success in almost all domains, including education, transportation, healthcare, energy…
EvalQReason: A Framework for Step-Level Reasoning Evaluation in Large Language Models
Shaima Ahmad Freja, Ferhat Ozgur Catak, Betul Yurdem +1
Large Language Models (LLMs) are increasingly deployed in critical applications requiring reliable reasoning, yet their internal reasoning processes remain difficult to evaluate sy…
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong +2
Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but is challenged by client drift in highly heterogeneous data settings. Many existing dri…
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong
As privacy concerns and data regulations grow, federated learning (FL) has emerged as a promising approach for training machine learning models across decentralized data sources wi…
Classification with Extreme Learning Machine and Ensemble Algorithms Over Randomly Partitioned Data
Ferhat Özgür Çatak
In this age of Big Data, machine learning based data mining methods are extensively used to inspect large scale data sets. Deriving applicable predictive modeling from these type o…