Zobrazeno 1 - 10
of 24 777
pro vyhledávání: '"Estimation of Distribution"'
Autor:
Liao, Weizhi1 (AUTHOR) liaowz@zjxu.edu.cn, Jin, Youzhen1 (AUTHOR) jinritian521@sina.com, Wang, Zijia2 (AUTHOR) jinritian521@sina.com, Wang, Xue3 (AUTHOR) wangxuezstu@163.com, Xia, Xiaoyun1 (AUTHOR)
Publikováno v:
Biomimetics (2313-7673). Nov2024, Vol. 9 Issue 11, p652. 19p.
Autor:
Gardiner, John, Lopez-Piqueres, Javier
Tensor networks are a tool first employed in the context of many-body quantum physics that now have a wide range of uses across the computational sciences, from numerical methods to machine learning. Methods integrating tensor networks into evolution
Externí odkaz:
http://arxiv.org/abs/2412.19780
Real-life batteries tend to experience a range of operating conditions, and undergo degradation due to a combination of both calendar and cycling aging. Onboard health estimation models typically use cycling aging data only, and account for at most o
Externí odkaz:
http://arxiv.org/abs/2410.15271
Publikováno v:
In Applied Soft Computing January 2025 169
Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms, providing effective and efficient optimization performance in a variety of research areas. Recent studies have proposed new EDAs that employ
Externí odkaz:
http://arxiv.org/abs/2407.18257
Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the problem is known to be multi-level as the performance of a given architectu
Externí odkaz:
http://arxiv.org/abs/2407.20091
Exact computation of various machine learning explanations requires numerous model evaluations and in extreme cases becomes impractical. The computational cost of approximation increases with an ever-increasing size of data and model parameters. Many
Externí odkaz:
http://arxiv.org/abs/2406.18334
Leveraging the models' outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples without requiring access to the corresponding ground truth labels. Desp
Externí odkaz:
http://arxiv.org/abs/2405.18979
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