Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Pedrielli, Giulia"'
Black-box optimization is ubiquitous in machine learning, operations research and engineering simulation. Black-box optimization algorithms typically do not assume structural information about the objective function and thus must make use of stochast
Externí odkaz:
http://arxiv.org/abs/2407.13576
The performance of modern machine learning algorithms depends upon the selection of a set of hyperparameters. Common examples of hyperparameters are learning rate and the number of layers in a dense neural network. Auto-ML is a branch of optimization
Externí odkaz:
http://arxiv.org/abs/2312.17404
Autor:
Zhou, You1,2 (AUTHOR), Pedrielli, Giulia1,2 (AUTHOR) Giulia.Pedrielli@asu.edu, Zhang, Fei3 (AUTHOR), Wu, Teresa1,2 (AUTHOR)
Publikováno v:
BMC Bioinformatics. 9/30/2024, Vol. 25 Issue 1, p1-26. 26p.
Bayesian optimization (BO) has been widely used in machine learning and simulation optimization. With the increase in computational resources and storage capacities in these fields, high-dimensional and large-scale problems are becoming increasingly
Externí odkaz:
http://arxiv.org/abs/2205.07525
Autor:
Xie, Wei, Pedrielli, Giulia
The increasingly pressing demand of novel drugs (e.g., gene therapies for personalized cancer care, ever evolving vaccines) with unprecedented levels of personalization, has put a remarkable pressure on the traditionally long time required by the pha
Externí odkaz:
http://arxiv.org/abs/2205.03920
Autor:
Pedrielli, Giulia, Khandait, Tanmay, Chotaliya, Surdeep, Thibeault, Quinn, Huang, Hao, Castillo-Effen, Mauricio, Fainekos, Georgios
Requirements driven search-based testing (also known as falsification) has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems. Despite the constant improvements on the performance and applicabi
Externí odkaz:
http://arxiv.org/abs/2110.10729
Autor:
Thibeault, Quinn, Anderson, Jacob, Chandratre, Aniruddh, Pedrielli, Giulia, Fainekos, Georgios
In this paper, we present the Python package PSY-TaLiRo which is a toolbox for temporal logic robustness guided falsification of Cyber-Physical Systems (CPS). PSY-TaLiRo is a completely modular toolbox supporting multiple temporal logic offline monit
Externí odkaz:
http://arxiv.org/abs/2106.02200
Autor:
Boyle, Esther, Inanlouganji, Alireza, Carvalhaes, Thomaz, Jevtić, Petar, Pedrielli, Giulia, Reddy, T. Agami
Publikováno v:
In International Journal of Disaster Risk Reduction November 2022 82
Publikováno v:
In Transportation Research Part E July 2022 163
Autor:
Awada, Mohamad, Becerik-Gerber, Burçin, White, Elizabeth, Hoque, Simi, O'Neill, Zheng, Pedrielli, Giulia, Wen, Jin, Wu, Teresa
Publikováno v:
In Building and Environment January 2022 207 Part A