Zobrazeno 1 - 10
of 42 381
pro vyhledávání: '"Graph models"'
The increasing complexity of modern processor and IP designs presents significant challenges in identifying and mitigating hardware flaws early in the IC design cycle. Traditional hardware fuzzing techniques, inspired by software testing, have shown
Externí odkaz:
http://arxiv.org/abs/2412.13374
With the trend of large graph learning models, business owners tend to employ a model provided by a third party to deliver business services to users. However, these models might be backdoored, and malicious users can submit trigger-embedded inputs t
Externí odkaz:
http://arxiv.org/abs/2410.04916
Hydrocarbon pyrolysis is a complex chemical reaction system at extreme temperature and pressure conditions involving large numbers of chemical reactions and chemical species. Only two kinds of atoms are involved: carbons and hydrogens. Its effective
Externí odkaz:
http://arxiv.org/abs/2409.19141
In the 20th century, individual technology products like the generator, telephone, and automobile were connected to form many of the large-scale, complex, infrastructure networks we know today: the power grid, the communication infrastructure, and th
Externí odkaz:
http://arxiv.org/abs/2409.03630
Publikováno v:
PLoS ONE. 12/17/2024, Vol. 19 Issue 12, p1-12. 12p.
This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshe
Externí odkaz:
http://arxiv.org/abs/2406.00081
Exploring Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms
Desirable random graph models (RGMs) should (i) generate realistic structures such as high clustering (i.e., high subgraph densities), (ii) generate variable (i.e., not overly similar) graphs, and (iii) remain tractable to compute and control graph s
Externí odkaz:
http://arxiv.org/abs/2405.16726
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:
Buttazzo, Sergio, Kauermann, Göran
The paper demonstrates the use of LASSO-based estimation in network models. Taking the Exponential Random Graph Model (ERGM) as a flexible and widely used model for network data analysis, the paper focuses on the question of how to specify the (suffi
Externí odkaz:
http://arxiv.org/abs/2407.15674
Autor:
Wang, Yu, Rossi, Ryan A., Park, Namyong, Chen, Huiyuan, Ahmed, Nesreen K., Trivedi, Puja, Dernoncourt, Franck, Koutra, Danai, Derr, Tyler
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous domains. This training paradigm over diverse well-curated
Externí odkaz:
http://arxiv.org/abs/2406.05109