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pro vyhledávání: '"Ajroldi, Niccolò"'
Optimization methods play a crucial role in modern machine learning, powering the remarkable empirical achievements of deep learning models. These successes are even more remarkable given the complex non-convex nature of the loss landscape of these m
Externí odkaz:
http://arxiv.org/abs/2410.12455
Publikováno v:
Computational Statistics & Data Analysis, 2023, 107821, ISSN 0167-9473
Time evolving surfaces can be modeled as two-dimensional Functional time series, exploiting the tools of Functional data analysis. Leveraging this approach, a forecasting framework for such complex data is developed. The main focus revolves around Co
Externí odkaz:
http://arxiv.org/abs/2207.13656
Publikováno v:
In Computational Statistics and Data Analysis November 2023 187