Zobrazeno 1 - 10
of 4 631
pro vyhledávání: '"Salami, P."'
Autor:
Pargoo, Navid Salami, Ghasemi, Mahshid, Xia, Shuren, Turkcan, Mehmet Kerem, Ehsan, Taqiya, Zang, Chengbo, Sun, Yuan, Ghaderi, Javad, Zussman, Gil, Kostic, Zoran, Ortiz, Jorge
As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that
Externí odkaz:
http://arxiv.org/abs/2411.19714
Autor:
Salami, Riccardo, Buzzega, Pietro, Mosconi, Matteo, Bonato, Jacopo, Sabetta, Luigi, Calderara, Simone
Model merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving performance and scalability. In this respect, the compositional properties of low-rank adaptation te
Externí odkaz:
http://arxiv.org/abs/2410.17961
Autor:
Wynne, Brian, Saenz, Francisco, Al-Salami, Jabir, Xu, Yufan, Sun, Zhen, Hu, Changhong, Hanada, Kazuaki, Kolemen, Egemen
The extreme heat fluxes in the divertor region of tokamaks may require an alternative to solid plasma-facing components, for the extraction of heat and the protection of the surrounding walls. Flowing liquid metals are proposed as an alternative, but
Externí odkaz:
http://arxiv.org/abs/2409.08950
Complex human activity recognition (CHAR) remains a pivotal challenge within ubiquitous computing, especially in the context of smart environments. Existing studies typically require meticulous labeling of both atomic and complex activities, a task t
Externí odkaz:
http://arxiv.org/abs/2407.03291
Reinforcement Learning from Human Feedback (RLHF) is popular in large language models (LLMs), whereas traditional Reinforcement Learning (RL) often falls short. Current autonomous driving methods typically utilize either human feedback in machine lea
Externí odkaz:
http://arxiv.org/abs/2406.04481
Federated Learning (FL) aims at unburdening the training of deep models by distributing computation across multiple devices (clients) while safeguarding data privacy. On top of that, Federated Continual Learning (FCL) also accounts for data distribut
Externí odkaz:
http://arxiv.org/abs/2406.02447
General purpose Large Language Models (LLM) such as the Generative Pretrained Transformer (GPT) and Large Language Model Meta AI (LLaMA) have attracted much attention in recent years. There is strong evidence that these models can perform remarkably
Externí odkaz:
http://arxiv.org/abs/2404.15578
The increasing cloudification and softwarization of networks foster the interplay among multiple independently managed deployments. An appealing reason for such an interplay lies in distributed Machine Learning (ML), which allows the creation of robu
Externí odkaz:
http://arxiv.org/abs/2405.05140
Autor:
Karchkhadze, Tornike, Kavaki, Hassan Salami, Izadi, Mohammad Rasool, Irvin, Bryce, Kegler, Mikolaj, Hertz, Ari, Zhang, Shuo, Stamenovic, Marko
Publikováno v:
EUSIPCO 2024 Proceedings, ISBN: 978-9-4645-9361-7
Foley sound generation, the art of creating audio for multimedia, has recently seen notable advancements through text-conditioned latent diffusion models. These systems use multimodal text-audio representation models, such as Contrastive Language-Aud
Externí odkaz:
http://arxiv.org/abs/2403.12182
In our pursuit of quantum supremacy during the NISQ era, this research introduces a novel approach rooted in the Quantum Approximate Optimization Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By strategically reducing th
Externí odkaz:
http://arxiv.org/abs/2402.18530