Zobrazeno 1 - 10
of 22 626
pro vyhledávání: '"A. Melek"'
Autor:
Al-Sharman, Mohammad, Edes, Luc, Sun, Bert, Jayakumar, Vishal, Daoud, Mohamed A., Rayside, Derek, Melek, William
Autonomous driving at unsignalized intersections is still considered a challenging application for machine learning due to the complications associated with handling complex multi-agent scenarios characterized by a high degree of uncertainty. Automat
Externí odkaz:
http://arxiv.org/abs/2409.13144
Learning-based methods have proven useful at generating complex motions for robots, including humanoids. Reinforcement learning (RL) has been used to learn locomotion policies, some of which leverage a periodic reward formulation. This work extends t
Externí odkaz:
http://arxiv.org/abs/2409.07846
Autor:
Taiello, Riccardo, Cansiz, Sergen, Vesin, Marc, Cremonesi, Francesco, Innocenti, Lucia, Önen, Melek, Lorenzi, Marco
Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the study of
Externí odkaz:
http://arxiv.org/abs/2409.00974
Assessing seismic hazards and thereby designing earthquake-resilient structures or evaluating structural damage that has been incurred after an earthquake are important objectives in earthquake engineering. Both tasks require critical evaluation of s
Externí odkaz:
http://arxiv.org/abs/2408.14962
Humanoid locomotion is a key skill to bring humanoids out of the lab and into the real-world. Many motion generation methods for locomotion have been proposed including reinforcement learning (RL). RL locomotion policies offer great versatility and g
Externí odkaz:
http://arxiv.org/abs/2407.05148
Autor:
Türkmen, Melek, Meral, Sanem, Yilmaz, Baris, Cikis, Melis, Akagündüz, Erdem, Tileylioglu, Salih
This paper explores the application of deep learning (DL) techniques to strong motion records for single-station epicenter localization. Often underutilized in seismology-related studies, strong motion records offer a potential wealth of information
Externí odkaz:
http://arxiv.org/abs/2405.18451
Autor:
Werner, Alexander, Melek, William
This paper presents an approach to teleoperate a manipulator using a mobile phone as a leader device. Using its IMU and camera, the phone estimates its Cartesian pose which is then used to to control the Cartesian pose of the robot's tool. The user r
Externí odkaz:
http://arxiv.org/abs/2405.07128
Publikováno v:
African Journal of Reproductive Health / La Revue Africaine de la Santé Reproductive, 2024 Jul 01. 28(7), 71-82.
Externí odkaz:
https://www.jstor.org/stable/27321559
Autor:
Ünsal, Ayşe, Önen, Melek
This work inspects a privacy metric based on Chernoff information, \textit{Chernoff differential privacy}, due to its significance in characterization of the optimal classifier's performance. Adversarial classification, as any other classification pr
Externí odkaz:
http://arxiv.org/abs/2403.10307
Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks such as earthquake event classifi
Externí odkaz:
http://arxiv.org/abs/2403.07569