Zobrazeno 1 - 10
of 3 930
pro vyhledávání: '"P, Dominici"'
Autor:
Zorzetto, Dafne, Torre, Paolo Dalla, Petrone, Sonia, Dominici, Francesca, Bargagli-Stoffi, Falco J.
Principal stratification provides a robust causal inference framework for the adjustment of post-treatment variables when comparing the effects of a treatment in health and social sciences. In this paper, we introduce a novel Bayesian nonparametric m
Externí odkaz:
http://arxiv.org/abs/2412.00311
Studies investigating the causal effects of spatially varying exposures on health$\unicode{x2013}$such as air pollution, green space, or crime$\unicode{x2013}$often rely on observational and spatially indexed data. A prevalent challenge is unmeasured
Externí odkaz:
http://arxiv.org/abs/2411.10381
Autor:
Guidi, Gianluca, Dominici, Francesca, Gilmour, Jonathan, Butler, Kevin, Bell, Eric, Delaney, Scott, Bargagli-Stoffi, Falco J.
The rapid proliferation of data centers in the US - driven partly by the adoption of artificial intelligence - has set off alarm bells about the industry's environmental impact. We compiled detailed information on 2,132 US data centers operating betw
Externí odkaz:
http://arxiv.org/abs/2411.09786
Autor:
De Bona, Francesco Bombassei, Dominici, Gabriele, Miller, Tim, Langheinrich, Marc, Gjoreski, Martin
As AI becomes fundamental in sectors like healthcare, explainable AI (XAI) tools are essential for trust and transparency. However, traditional user studies used to evaluate these tools are often costly, time consuming, and difficult to scale. In thi
Externí odkaz:
http://arxiv.org/abs/2410.17781
The electric power sector is one of the largest contributors to greenhouse gas emissions in the world. In recent years, there has been an unprecedented increase in electricity demand driven by the so-called Artificial Intelligence (AI) revolution. Al
Externí odkaz:
http://arxiv.org/abs/2410.09029
Topological Deep Learning (TDL) has emerged as a paradigm to process and learn from signals defined on higher-order combinatorial topological spaces, such as simplicial or cell complexes. Although many complex systems have an asymmetric relational st
Externí odkaz:
http://arxiv.org/abs/2409.08389
Autor:
Koprucu, Nursena, Nigam, Meher Shashwat, Xu, Shicheng, Abere, Biruk, Dominici, Gabriele, Rodriguez, Andrew, Vadgama, Sharvaree, Inal, Berfin, Tono, Alberto
Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize shapes, first learn to generate them, we explore the use of 3D diffusion models for object classification. Leveraging the density estimates from these models, our approach, the
Externí odkaz:
http://arxiv.org/abs/2408.06693
Autor:
Zorzetto, Dafne, Canale, Antonio, Mealli, Fabrizia, Dominici, Francesca, Bargagli-Stoffi, Falco J.
Principal stratification provides a causal inference framework that allows adjustment for confounded post-treatment variables when comparing treatments. Although the literature has focused mainly on binary post-treatment variables, there is a growing
Externí odkaz:
http://arxiv.org/abs/2405.17669
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