Zobrazeno 1 - 10
of 283
pro vyhledávání: '"Xu, Pengxiang"'
The J-orthogonal matrix, also referred to as the hyperbolic orthogonal matrix, is a class of special orthogonal matrix in hyperbolic space, notable for its advantageous properties. These matrices are integral to optimization under J-orthogonal constr
Externí odkaz:
http://arxiv.org/abs/2406.09771
Autor:
Cui, Xiaojie1,2 (AUTHOR) cuixiaojie3911@hotmail.com, Xu, Pengxiang1,2 (AUTHOR), Tian, Tao1,2 (AUTHOR), Song, Mingyuan1,2 (AUTHOR), Qin, Xuyang1,2 (AUTHOR), Gong, Dehua1,2 (AUTHOR), Wang, Yan1,2 (AUTHOR), Zhang, Xuguang3 (AUTHOR), Xing, Binbin1,2 (AUTHOR), Li, Mingzhi4 (AUTHOR) limingzhi@dlou.edu.cn, Yin, Leiming1,2 (AUTHOR) limingzhi@dlou.edu.cn
Publikováno v:
Fishes (MDPI AG). Jun2024, Vol. 9 Issue 6, p217. 13p.
Publikováno v:
In Computer Vision and Image Understanding December 2024 249
In this paper, a novel second-order method called NG+ is proposed. By following the rule ``the shape of the gradient equals the shape of the parameter", we define a generalized fisher information matrix (GFIM) using the products of gradients in the m
Externí odkaz:
http://arxiv.org/abs/2106.07454
Publikováno v:
In Materials Today Communications March 2024 38
Autor:
Zhang, Dongli, Zhou, Haibin, Ding, Jingtao, Shen, Yujun, hong Zhang, Yue, Cheng, Qiongyi, Zhang, Yang, Ma, Shuangshuang, Feng, Qikun, Xu, Pengxiang
Publikováno v:
In Bioresource Technology March 2024 396
Training a deep neural network heavily relies on a large amount of training data with accurate annotations. To alleviate this problem, various methods have been proposed to annotate the data automatically. However, automatically generating annotation
Externí odkaz:
http://arxiv.org/abs/2103.00813
Autor:
Ma, Shuangshuang, Shen, Yujun, Ding, Jingtao, Cheng, Hongsheng, Zhou, Haibin, Ge, Mianshen, Wang, Jian, Cheng, Qiongyi, Zhang, Dongli, Zhang, Yun, Xu, Pengxiang, Zhang, Pengyue
Publikováno v:
In Bioresource Technology January 2024 391 Part A
Publikováno v:
Quantum Engineering.(2021) 1-14
Variational quantum eigensolver (VQE) is promising to show quantum advantage on near-term noisy-intermediate-scale quantum (NISQ) computers. One central problem of VQE is the effect of noise, especially the physical noise on realistic quantum compute
Externí odkaz:
http://arxiv.org/abs/2010.14821
Publikováno v:
Quantum Sci. Technol. 6 (2021) 045009
Hamiltonian diagonalization is at the heart of understanding physical properties and practical applications of quantum systems. It is highly desired to design quantum algorithms that can speedup Hamiltonian diagonalization, especially those can be im
Externí odkaz:
http://arxiv.org/abs/2008.09854