Zobrazeno 1 - 10
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pro vyhledávání: '"YAVUZ, And"'
Large language models (LLMs) have demonstrated strong capabilities in text understanding and generation. However, they often lack factuality, producing a mixture of true and false information, especially in long-form generation. In this work, we inve
Externí odkaz:
http://arxiv.org/abs/2411.15993
Autor:
Liu, Ye, Meng, Rui, Joty, Shafiq, Savarese, Silvio, Xiong, Caiming, Zhou, Yingbo, Yavuz, Semih
Despite the success of text retrieval in many NLP tasks, code retrieval remains a largely underexplored area. Most text retrieval systems are tailored for natural language queries, often neglecting the specific challenges of retrieving code. This gap
Externí odkaz:
http://arxiv.org/abs/2411.12644
This study proposes a novel approach for real-time facial expression recognition utilizing short-range Frequency-Modulated Continuous-Wave (FMCW) radar equipped with one transmit (Tx), and three receive (Rx) antennas. The system leverages four distin
Externí odkaz:
http://arxiv.org/abs/2411.11619
Autor:
Pan, Hongyi, Hong, Ziliang, Durak, Gorkem, Keles, Elif, Aktas, Halil Ertugrul, Taktak, Yavuz, Medetalibeyoglu, Alpay, Zhang, Zheyuan, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Miller, Frank, Keswani, Rajesh N., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification ut
Externí odkaz:
http://arxiv.org/abs/2411.05697
Autor:
Yavuz, Mehmet Can, Yang, Yang
In biomedical imaging analysis, the dichotomy between 2D and 3D data presents a significant challenge. While 3D volumes offer superior real-world applicability, they are less available for each modality and not easy to train in large scale, whereas 2
Externí odkaz:
http://arxiv.org/abs/2411.02441
Heterogeneous Internet of Things (IoTs) harboring resource-limited devices like wearable sensors are essential for next-generation networks. Ensuring the authentication and integrity of security-sensitive telemetry in these applications is vital. Dig
Externí odkaz:
http://arxiv.org/abs/2411.01380
Accurate document retrieval is crucial for the success of retrieval-augmented generation (RAG) applications, including open-domain question answering and code completion. While large language models (LLMs) have been employed as dense encoders or list
Externí odkaz:
http://arxiv.org/abs/2411.00142
Autor:
Pan, Hongyi, Durak, Gorkem, Zhang, Zheyuan, Taktak, Yavuz, Keles, Elif, Aktas, Halil Ertugrul, Medetalibeyoglu, Alpay, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Keswani, Rajesh N., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Goggins, Michael G., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face d
Externí odkaz:
http://arxiv.org/abs/2410.22530
Autor:
Eyiokur, Fevziye Irem, Huber, Christian, Nguyen, Thai-Binh, Nguyen, Tuan-Nam, Retkowski, Fabian, Ugan, Enes Yavuz, Yaman, Dogucan, Waibel, Alexander
In this paper, we report on communication experiments conducted in the summer of 2022 during a deep dive to the wreck of the Titanic. Radio transmission is not possible in deep sea water, and communication links rely on sonar signals. Due to the low
Externí odkaz:
http://arxiv.org/abs/2410.11434
Federated learning enables the collaborative learning of a global model on diverse data, preserving data locality and eliminating the need to transfer user data to a central server. However, data privacy remains vulnerable, as attacks can target user
Externí odkaz:
http://arxiv.org/abs/2410.09676