Zobrazeno 1 - 10
of 11 439
pro vyhledávání: '"data-driven learning"'
A semigroup characterization, or equivalently, a characterization by the generator, is a classical technique used to describe continuous-time nonlinear dynamical systems. In the realm of data-driven learning for an unknown nonlinear system, one must
Externí odkaz:
http://arxiv.org/abs/2411.00923
The mathematical modeling of crowds is complicated by the fact that crowds possess the behavioral ability to develop and adapt moving strategies in response to the context. For example, in emergency situations, people tend to alter their walking stra
Externí odkaz:
http://arxiv.org/abs/2411.12974
We develop an efficient data-driven and model-free unsupervised learning algorithm for achieving fully passive intelligent reflective surface (IRS)-assisted optimal short/long-term beamforming in wireless communication networks. The proposed algorith
Externí odkaz:
http://arxiv.org/abs/2410.24154
Robotic manipulation is essential for modernizing factories and automating industrial tasks like polishing, which require advanced tactile abilities. These robots must be easily set up, safely work with humans, learn tasks autonomously, and transfer
Externí odkaz:
http://arxiv.org/abs/2408.12285
Autor:
Rajab Esfandiari, Omid Allaf-Akbary
Publikováno v:
Language Testing in Asia, Vol 14, Iss 1, Pp 1-30 (2024)
Abstract The purpose of the current study was twofold: examining the efficacy of data-driven learning (DDL) (hands-on and hands-off approaches) in the realization of interactional metadiscourse markers (IMMs) among English as a foreign language (EFL)
Externí odkaz:
https://doaj.org/article/9cdb53070de24a9f9d13e355d6cf284f
Contrary to on-road autonomous navigation, off-road autonomy is complicated by various factors ranging from sensing challenges to terrain variability. In such a milieu, data-driven approaches have been commonly employed to capture intricate vehicle-e
Externí odkaz:
http://arxiv.org/abs/2409.10347
We propose a novel machine learning approach for inferring causal variables of a target variable from observations. Our goal is to identify both direct and indirect causes within a system, thereby efficiently regulating the target variable when the d
Externí odkaz:
http://arxiv.org/abs/2408.16218
Autor:
Mishra, Amish, Motta, Francis
In this paper, we propose a data-driven method to learn interpretable topological features of biomolecular data and demonstrate the efficacy of parsimonious models trained on topological features in predicting the stability of synthetic mini proteins
Externí odkaz:
http://arxiv.org/abs/2408.04847
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