Zobrazeno 1 - 10
of 1 003
pro vyhledávání: '"Barbiero, P."'
Clustering algorithms rely on complex optimisation processes that may be difficult to comprehend, especially for individuals who lack technical expertise. While many explainable artificial intelligence techniques exist for supervised machine learning
Externí odkaz:
http://arxiv.org/abs/2409.12632
Geometrical frustration and long-range couplings are key contributors to create quantum phases with different properties throughout physics. We propose a scheme where both ingredients naturally emerge in a Raman induced subwavelength lattice. We firs
Externí odkaz:
http://arxiv.org/abs/2409.01443
Autor:
Debot, David, Barbiero, Pietro, Giannini, Francesco, Ciravegna, Gabriele, Diligenti, Michelangelo, Marra, Giuseppe
The lack of transparency in the decision-making processes of deep learning systems presents a significant challenge in modern artificial intelligence (AI), as it impairs users' ability to rely on and verify these systems. To address this challenge, C
Externí odkaz:
http://arxiv.org/abs/2407.15527
Autor:
Baldelli, Niccolò, Montorsi, Arianna, Julià-Farré, Sergi, Lewenstein, Maciej, Rizzi, Matteo, Barbiero, Luca
Deconfined quantum critical points are exotic transition points not predicted by the Landau-Ginzburg-Wilson symmetry-breaking paradigm. They are associated to a one-point gap closing between distinct locally ordered phases, thus to a continuous phase
Externí odkaz:
http://arxiv.org/abs/2407.04073
Autor:
De Santis, Francesco, Bich, Philippe, Ciravegna, Gabriele, Barbiero, Pietro, Giordano, Danilo, Cerquitelli, Tania
Despite their success, Large-Language Models (LLMs) still face criticism as their lack of interpretability limits their controllability and reliability. Traditional post-hoc interpretation methods, based on attention and gradient-based analysis, offe
Externí odkaz:
http://arxiv.org/abs/2406.14335
Autor:
Dominici, Gabriele, Barbiero, Pietro, Giannini, Francesco, Gjoreski, Martin, Langhenirich, Marc
Interpretable deep learning aims at developing neural architectures whose decision-making processes could be understood by their users. Among these techniqes, Concept Bottleneck Models enhance the interpretability of neural networks by integrating a
Externí odkaz:
http://arxiv.org/abs/2405.16508
Autor:
Dominici, Gabriele, Barbiero, Pietro, Zarlenga, Mateo Espinosa, Termine, Alberto, Gjoreski, Martin, Marra, Giuseppe, Langheinrich, Marc
Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to rely on and verify state-of-the-art DNN-based systems, especially in hig
Externí odkaz:
http://arxiv.org/abs/2405.16507
Autor:
Fenoglio, Dario, Dominici, Gabriele, Barbiero, Pietro, Tonda, Alberto, Gjoreski, Martin, Langheinrich, Marc
Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks. However, enabling human trust and contr
Externí odkaz:
http://arxiv.org/abs/2405.15632
In recent years, the systems comprising of bosonic atoms confined to optical lattices at ultra-cold temperatures have demonstrated tremendous potential to unveil novel quantum mechanical effects appearing in lattice boson models with various kinds of
Externí odkaz:
http://arxiv.org/abs/2405.07775
Order parameters represent a fundamental resource to characterize quantum matter. We show that pair superfluids can be rigorously defined in terms of a nonlocal order parameter, named odd parity, which derivation is experimentally accessible by local
Externí odkaz:
http://arxiv.org/abs/2404.15972