Zobrazeno 1 - 10
of 40 559
pro vyhledávání: '"Balazs, A."'
Autor:
Bácsi, Ádám, Dóra, Balázs
We study the non-equilibrium dynamics of the superconducting order parameter in the Hatsugai-Kohmoto (HK) model. In the absence of superconductivity, its ground state is a non-Fermi liquid, whose properties are controlled by the HK interaction. Our p
Externí odkaz:
http://arxiv.org/abs/2410.14424
Autor:
Gyöngyössy, Natabara Máté, Török, Bernát, Farkas, Csilla, Lucaj, Laura, Menyhárd, Attila, Menyhárd-Balázs, Krisztina, Simonyi, András, van der Smagt, Patrick, Ződi, Zsolt, Lőrincz, András
Regulatory frameworks for the use of AI are emerging. However, they trail behind the fast-evolving malicious AI technologies that can quickly cause lasting societal damage. In response, we introduce a pioneering Assistive AI framework designed to enh
Externí odkaz:
http://arxiv.org/abs/2410.14353
Autor:
Benechehab, Abdelhakim, Hili, Youssef Attia El, Odonnat, Ambroise, Zekri, Oussama, Thomas, Albert, Paolo, Giuseppe, Filippone, Maurizio, Redko, Ievgen, Kégl, Balázs
The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensively used in text-based enviro
Externí odkaz:
http://arxiv.org/abs/2410.11711
Calibrating verbalized probabilities presents a novel approach for reliably assessing and leveraging outputs from black-box Large Language Models (LLMs). Recent methods have demonstrated improved calibration by applying techniques like Platt scaling
Externí odkaz:
http://arxiv.org/abs/2410.06707
Autor:
Recasens, Pol G., Horváth, Ádám, Gutierrez-Torre, Alberto, Torres, Jordi, Berral, Josep Ll., Pejó, Balázs
Federated learning is increasingly popular as it enables multiple parties with limited datasets and resources to train a high-performing machine learning model collaboratively. However, similarly to other collaborative systems, federated learning is
Externí odkaz:
http://arxiv.org/abs/2410.05020
Autor:
Keller, Kai, Yashiro, Hisashi, Wahib, Mohamed, Gerofi, Balazs, Kestelman, Adrian Cristal, Bautista-Gomez, Leonardo
Ensemble data assimilation techniques form an indispensable part of numerical weather prediction. As the ensemble size grows and model resolution increases, the amount of required storage becomes a major issue. Data compression schemes may come to th
Externí odkaz:
http://arxiv.org/abs/2410.03184
Autor:
Pentek, Balazs, Ercsey-Ravasz, Maria
Studying structural brain networks has witnessed significant advancement in recent decades. Findings have revealed a geometric principle, the exponential distance rule (EDR) showing that the number of neurons decreases exponentially with the length o
Externí odkaz:
http://arxiv.org/abs/2410.01269
Maintaining numerical stability in machine learning models is crucial for their reliability and performance. One approach to maintain stability of a network layer is to integrate the condition number of the weight matrix as a regularizing term into t
Externí odkaz:
http://arxiv.org/abs/2410.00169
Autor:
Albert, Joshua, Balazs, Csaba, Fowlie, Andrew, Handley, Will, Hunt-Smith, Nicholas, de Austri, Roberto Ruiz, White, Martin
For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare a wide ran
Externí odkaz:
http://arxiv.org/abs/2409.18464
We calculate the gravitational wave spectrum generated by sound waves during a cosmological phase transition, incorporating several advancements beyond the current state-of-the-art. Rather than relying on the bag model or similar approximations, we d
Externí odkaz:
http://arxiv.org/abs/2409.14505