1 citations · 1 across the 5 of their papers we have counts for
6 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…
Can Multimodal Large Language Models Understand Pathologic Movements? A Pilot Study on Seizure Semiology
Lina Zhang, Tonmoy Monsoor, Mehmet Efe Lorasdagi +8
Multimodal Large Language Models (MLLMs) have demonstrated robust capabilities in recognizing everyday human activities, yet their potential for analyzing clinically significant in…
Enhancing Deep Deterministic Policy Gradients on Continuous Control Tasks with Decoupled Prioritized Experience Replay
Mehmet Efe Lorasdagi, Dogan Can Cicek, Furkan Burak Mutlu +1
Background: Deep Deterministic Policy Gradient-based reinforcement learning algorithms utilize Actor-Critic architectures, where both networks are typically trained using identical…
Fitting Multiple Machine Learning Models with Performance Based Clustering
Mehmet Efe Lorasdagi, Ahmet Berker Koc, Ali Taha Koc +1
Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism…
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…