1 citations · 1 across the 6 of their papers we have counts for
7 papers
PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces
M. Yigit Turali, Shreyas Rajesh, M. Efe Lorasdagi +3
Deep research requires connecting multiple steps of search, reasoning, and computation, with intermediate findings guiding what to investigate next. As investigations grow, agents…
Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care
Shreyas Rajesh, Kartik Sharma, Tonmoy Monsoor +8
Specialist epilepsy expertise is scarce in resource-constrained settings. Clinical decision-support systems must adapt to local prescribing practices and recognize when specialist…
Panini: Continual Learning in Token Space via Structured Memory
Shreyas Rajesh, Pavan Holur, Mehmet Yigit Turali +2
Language models are increasingly used to reason over content they were not trained on, such as new documents, evolving knowledge, and user-specific data. A common approach is retri…
Customizing Open Source LLMs for Quantitative Medication Attribute Extraction across Heterogeneous EHR Systems
Zhe Fei, Mehmet Yigit Turali, Shreyas Rajesh +7
Harmonizing medication data across Electronic Health Record (EHR) systems is a persistent barrier to monitoring medications for opioid use disorder (MOUD). In heterogeneous EHR sys…
Binary Feature Mask Optimization for Feature Selection
Mehmet E. Lorasdagi, Mehmet Y. Turali, Suleyman S. Kozat
We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framew…
AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking
Mehmet Y. Turali, Mehmet E. Lorasdagi, Ali T. Koc +1
We study the problem of feature selection in general machine learning (ML) context, which is one of the most critical subjects in the field. Although, there exist many feature sele…
JointNET: A Deep Model for Predicting Active Sacroiliitis from Sacroiliac Joint Radiography
Sevcan Turk, Ahmet Demirkaya, M Yigit Turali +9
Purpose: To develop a deep learning model that predicts active inflammation from sacroiliac joint radiographs and to compare the success with radiologists. Materials and Methods: A…