Zobrazeno 1 - 10
of 2 119
pro vyhledávání: '"Franzese, P."'
Autor:
Montero, Mariano Ramírez, Shahabi, Ebrahim, Franzese, Giovanni, Kober, Jens, Mazzolai, Barbara, Della Santina, Cosimo
Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, but their precise control remains challenging. In contrast, traditional
Externí odkaz:
http://arxiv.org/abs/2410.07787
In this work we study how diffusion-based generative models produce high-dimensional data, such as an image, by implicitly relying on a manifestation of a low-dimensional set of latent abstractions, that guide the generative process. We present a nov
Externí odkaz:
http://arxiv.org/abs/2410.03368
Autor:
Vanc, Petr, Franzese, Giovanni, Behrens, Jan Kristof, Della Santina, Cosimo, Stepanova, Karla, Kober, Jens
Learning from demonstration is a promising way of teaching robots new skills. However, a central problem when executing acquired skills is to recognize risks and failures. This is essential since the demonstrations usually cover only a few mostly suc
Externí odkaz:
http://arxiv.org/abs/2409.20173
Though there is much interest in fair AI systems, the problem of fairness noncompliance -- which concerns whether fair models are used in practice -- has received lesser attention. Zero-Knowledge Proofs of Fairness (ZKPoF) address fairness noncomplia
Externí odkaz:
http://arxiv.org/abs/2410.02777
Diffusion models for Text-to-Image (T2I) conditional generation have seen tremendous success recently. Despite their success, accurately capturing user intentions with these models still requires a laborious trial and error process. This challenge is
Externí odkaz:
http://arxiv.org/abs/2405.20759
Water's unique anomalies are vital in various applications and biological processes, yet the molecular mechanisms behind these anomalies remain debated, particularly in the metastable liquid phase under supercooling and stretching conditions. Experim
Externí odkaz:
http://arxiv.org/abs/2405.10181
Learning from Interactive Demonstrations has revolutionized the way non-expert humans teach robots. It is enough to kinesthetically move the robot around to teach pick-and-place, dressing, or cleaning policies. However, the main challenge is correctl
Externí odkaz:
http://arxiv.org/abs/2404.13458
Autor:
Christophorou, Christophoros, Ioannou, Iacovos, Vassiliou, Vasos, Christofi, Loizos, Vardakas, John S, Seder, Erin E, Chiasserini, Carla Fabiana, Iordache, Marius, Issaid, Chaouki Ben, Markopoulos, Ioannis, Franzese, Giulio, Järvet, Tanel, Verikoukis, Christos
In the upcoming 6G era, mobile networks must deal with more challenging applications (e.g., holographic telepresence and immersive communication) and meet far more stringent application requirements stemming along the edge-cloud continuum. These new
Externí odkaz:
http://arxiv.org/abs/2403.05277
The analysis of scientific data and complex multivariate systems requires information quantities that capture relationships among multiple random variables. Recently, new information-theoretic measures have been developed to overcome the shortcomings
Externí odkaz:
http://arxiv.org/abs/2402.05667
Autor:
Franzese, Olive, Dziedzic, Adam, Choquette-Choo, Christopher A., Thomas, Mark R., Kaleem, Muhammad Ahmad, Rabanser, Stephan, Fang, Congyu, Jha, Somesh, Papernot, Nicolas, Wang, Xiao
Collaborative machine learning (ML) is widely used to enable institutions to learn better models from distributed data. While collaborative approaches to learning intuitively protect user data, they remain vulnerable to either the server, the clients
Externí odkaz:
http://arxiv.org/abs/2310.16678