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pro vyhledávání: '"SÁNCHEZ, IGNACIO"'
Autor:
Nagel, Andrew M., Webster, Anne, Henry, Christopher, Storie, Christopher, Sanchez, Ignacio San-Miguel, Tsui, Olivier, Duffe, Jason, Dean, Andy
In Canada's northern regions, linear disturbances such as roads, seismic exploration lines, and pipelines pose a significant threat to the boreal woodland caribou population (Rangifer tarandus). To address the critical need for management of these di
Externí odkaz:
http://arxiv.org/abs/2409.12817
Autor:
Llorca, David Fernández, Hamon, Ronan, Junklewitz, Henrik, Grosse, Kathrin, Kunze, Lars, Seiniger, Patrick, Swaim, Robert, Reed, Nick, Alahi, Alexandre, Gómez, Emilia, Sánchez, Ignacio, Kriston, Akos
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements
Externí odkaz:
http://arxiv.org/abs/2403.14641
Autor:
Udias, Angel, Alonso-Ayuso, Antonio, Sanchez, Ignacio, Hernandez, Sonia, Castellanos, Maria Eugenia, Diez, Raquel Montes, Cano, Emilio Lopez
In this paper, we assess the efficacy of ChatGPT (version Feb 2023), a large-scale language model, in solving probability problems typically presented in introductory computer engineering exams. Our study comprised a set of 23 probability exercises a
Externí odkaz:
http://arxiv.org/abs/2310.05686
We propose that spontaneous folding and molecular evolution of biopolymers are two universal aspects that must concur for life to happen. These aspects are fundamentally related to the chemical composition of biopolymers and crucially depend on the s
Externí odkaz:
http://arxiv.org/abs/2310.00067
Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-s
Externí odkaz:
http://arxiv.org/abs/2303.12000
Publikováno v:
Journal of Artificial Intelligence Research, Vol. 76 (2023), pp. 613-644
New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in t
Externí odkaz:
http://arxiv.org/abs/2211.01817