Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Salonidis, Theodoros"'
Autor:
Abbas, Momin, Zhou, Yi, Ram, Parikshit, Baracaldo, Nathalie, Samulowitz, Horst, Salonidis, Theodoros, Chen, Tianyi
In-context learning (ICL) is a new paradigm for natural language processing that utilizes Generative Pre-trained Transformer (GPT)-like models. This approach uses prompts that include in-context demonstrations to generate the corresponding output for
Externí odkaz:
http://arxiv.org/abs/2401.12406
Autor:
Zhou, Yi, Ram, Parikshit, Salonidis, Theodoros, Baracaldo, Nathalie, Samulowitz, Horst, Ludwig, Heiko
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO solution framework that can address use cases of tabular dat
Externí odkaz:
http://arxiv.org/abs/2202.08338
An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitting and processing large data volumes at distant cloud-deployed servers
Externí odkaz:
http://arxiv.org/abs/2201.02840
Autor:
Zhou, Yi, Ram, Parikshit, Salonidis, Theodoros, Baracaldo, Nathalie, Samulowitz, Horst, Ludwig, Heiko
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss suRface Aggregation (FLoRA), the first FL-HPO solution framework that can address use cases of tabular dat
Externí odkaz:
http://arxiv.org/abs/2112.08524
Autor:
Choudhury, Olivia, Gkoulalas-Divanis, Aris, Salonidis, Theodoros, Sylla, Issa, Park, Yoonyoung, Hsu, Grace, Das, Amar
Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensi
Externí odkaz:
http://arxiv.org/abs/2002.09096
Autor:
Choudhury, Olivia, Gkoulalas-Divanis, Aris, Salonidis, Theodoros, Sylla, Issa, Park, Yoonyoung, Hsu, Grace, Das, Amar
Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraint
Externí odkaz:
http://arxiv.org/abs/1910.02578
This paper studies the problem of allocating bandwidth and computation resources to data analytics tasks in Internet of Things (IoT) networks. IoT nodes are powered by batteries, can process (some of) the data locally, and the quality grade or perfor
Externí odkaz:
http://arxiv.org/abs/1904.09827
Autor:
Wang, Shiqiang, Tuor, Tiffany, Salonidis, Theodoros, Leung, Kin K., Makaya, Christian, He, Ting, Chan, Kevin
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detect
Externí odkaz:
http://arxiv.org/abs/1804.05271
Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time applications, such as instantaneous object recognition and safety assis
Externí odkaz:
http://arxiv.org/abs/1706.04118
Autor:
Dehghan, Mostafa, Seetharam, Anand, Jiang, Bo, He, Ting, Salonidis, Theodoros, Kurose, Jim, Towsley, Don, Sitaraman, Ramesh
We investigate the problem of optimal request routing and content caching in a heterogeneous network supporting in-network content caching with the goal of minimizing average content access delay. Here, content can either be accessed directly from a
Externí odkaz:
http://arxiv.org/abs/1501.00216