Zobrazeno 1 - 10
of 529
pro vyhledávání: '"A, Camoriano"'
Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, particularly pronounced in the final classification layer, negatively imp
Externí odkaz:
http://arxiv.org/abs/2406.01116
Autor:
Maracani, Andrea, Camoriano, Raffaello, Maiettini, Elisa, Talon, Davide, Rosasco, Lorenzo, Natale, Lorenzo
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical understanding of the complex relationships between multiple key design fac
Externí odkaz:
http://arxiv.org/abs/2402.16090
Autor:
Duan, Anqing, Liuchen, Wanli, Wu, Jinsong, Camoriano, Raffaello, Rosasco, Lorenzo, Navarro-Alarcon, David
The increasing deployment of robots has significantly enhanced the automation levels across a wide and diverse range of industries. This paper investigates the automation challenges of laser-based dermatology procedures in the beauty industry; This g
Externí odkaz:
http://arxiv.org/abs/2312.13623
Autor:
Duan, Anqing, Batzianoulis, Iason, Camoriano, Raffaello, Rosasco, Lorenzo, Pucci, Daniele, Billard, Aude
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervi
Externí odkaz:
http://arxiv.org/abs/2309.14829
High Power Laser's (HPL) optimal performance is essential for the success of a wide variety of experimental tasks related to light-matter interactions. Traditionally, HPL parameters are optimised in an automated fashion relying on black-box numerical
Externí odkaz:
http://arxiv.org/abs/2304.12187
Autor:
Maracani, Andrea, Camoriano, Raffaello, Maiettini, Elisa, Talon, Davide, Rosasco, Lorenzo, Natale, Lorenzo
Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks. However, target domain labels are not accessible in many real-world scenarios. This led to the development of Uns
Externí odkaz:
http://arxiv.org/abs/2302.05379
Popular industrial robotic problems such as spray painting and welding require (i) conditioning on free-shape 3D objects and (ii) planning of multiple trajectories to solve the task. Yet, existing solutions make strong assumptions on the form of inpu
Externí odkaz:
http://arxiv.org/abs/2211.06930
Autor:
Ellen Meltzer, Laurie Wilshusen, Isra Abdulwadood, Claire Yee, Amy Sherman, Kelli Strader, Barbara Thomley, Denise Millstine, Jon Tilburt, Heather Fields, Larry Bergstrom, David Patchett, John Camoriano, Brent Bauer
Publikováno v:
JMIR Formative Research, Vol 8, p e56312 (2024)
BackgroundThe use of telemedicine (TELE) increased exponentially during the COVID-19 pandemic. While patient experience with TELE has been studied in other medical disciplines, its impact and applicability to integrative medicine practices remain unk
Externí odkaz:
https://doaj.org/article/68e2ca07faa94d0bbe5693de9ea8d55e
Autor:
Ferigo, Diego, Camoriano, Raffaello, Viceconte, Paolo Maria, Calandriello, Daniele, Traversaro, Silvio, Rosasco, Lorenzo, Pucci, Daniele
Publikováno v:
IEEE Robotics and Automation Letters (RA-L) 2021
Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successfu
Externí odkaz:
http://arxiv.org/abs/2104.14534
Publikováno v:
IEEE Robotics and Automation Letters (2021) and IEEE International Conference on Robotics and Automation (2021)
With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a red
Externí odkaz:
http://arxiv.org/abs/2102.12942