Zobrazeno 1 - 10
of 360
pro vyhledávání: '"MILES, KATE"'
Surrogate modelling techniques have seen growing attention in recent years when applied to both modelling and optimisation of industrial design problems. These techniques are highly relevant when assessing the performance of a particular design carri
Externí odkaz:
http://arxiv.org/abs/2403.08118
The performance of the Quantum Approximate Optimisation Algorithm (QAOA) relies on the setting of optimal parameters in each layer of the circuit. This is no trivial task, and much literature has focused on the challenge of finding optimal parameters
Externí odkaz:
http://arxiv.org/abs/2401.08142
Publikováno v:
IEEE Transactions on Software Engineering, 49(4), 2642-2660 (2022)
Search-based software testing (SBST) is now a mature area, with numerous techniques developed to tackle the challenging task of software testing. SBST techniques have shown promising results and have been successfully applied in the industry to autom
Externí odkaz:
http://arxiv.org/abs/2312.02392
Item Response Theory (IRT) has been proposed within the field of Educational Psychometrics to assess student ability as well as test question difficulty and discrimination power. More recently, IRT has been applied to evaluate machine learning algori
Externí odkaz:
http://arxiv.org/abs/2307.15850
Accurately detecting lane lines in 3D space is crucial for autonomous driving. Existing methods usually first transform image-view features into bird-eye-view (BEV) by aid of inverse perspective mapping (IPM), and then detect lane lines based on the
Externí odkaz:
http://arxiv.org/abs/2306.04927
Autor:
Shireen, Zakiya, Weeratunge, Hansani, Menzel, Adrian, Phillips, Andrew W, Larson, Ronald G, Smith-Miles, Kate, Hajizadeh, Elnaz
This work presents a novel framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical optimization appro
Externí odkaz:
http://arxiv.org/abs/2204.13295
For building successful Machine Learning (ML) systems, it is imperative to have high quality data and well tuned learning models. But how can one assess the quality of a given dataset? And how can the strengths and weaknesses of a model on a dataset
Externí odkaz:
http://arxiv.org/abs/2109.14430
Publikováno v:
In Journal of Hydrology June 2024 636
