1 citations · 2 across the 6 of their papers we have counts for
7 papers
Mammography Foundation Models for Opportunistic Prediction of Major Adverse Cardiovascular Events
Paula Feldman, Nusrat Binta Nizam, Sunwoo Kwak +3
Cardiovascular disease (CVD) remains the leading cause of death among women, yet cardiovascular risk assessment often relies on clinical variables that may be missing, outdated, or…
DISPO: Enhancing Training Efficiency and Stability in Reinforcement Learning for Large Language Model Mathematical Reasoning
Batuhan K. Karaman, Aditya Rawal, Suhaila Shakiah +4
Reinforcement learning with verifiable rewards has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models particularly in mathematics. Cu…
POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization
Batuhan K. Karaman, Ishmam Zabir, Alon Benhaim +3
Achieving both high safety and high usefulness simultaneously in large language models has become a critical challenge in recent years.Models often exhibit unsafe behavior or adopt…
Assessing the significance of longitudinal data in Alzheimer's Disease forecasting
Batuhan K. Karaman, Mert R. Sabuncu
In this study, we employ a transformer encoder model to characterize the significance of longitudinal patient data for forecasting the progression of Alzheimer's Disease (AD). Our…
Knockout: A simple way to handle missing inputs
Minh Nguyen, Batuhan K. Karaman, Heejong Kim +3
Deep learning models benefit from rich (e.g., multi-modal) input features. However, multimodal models might be challenging to deploy, because some inputs may be missing at inferenc…
Longitudinal Mammogram Risk Prediction
Batuhan K. Karaman, Katerina Dodelzon, Gozde B. Akar +1
Breast cancer is one of the leading causes of mortality among women worldwide. Early detection and risk assessment play a crucial role in improving survival rates. Therefore, annua…
A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang, Batuhan K. Karaman, Heejong Kim +4
Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretabilit…