Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Dong Shuyu"'
Publikováno v:
Nanophotonics, Vol 12, Iss 3, Pp 505-519 (2023)
Common methods to achieve photon number resolution rely on fast on-off single-photon detectors in conjunction with temporal or spatial mode multiplexing. Yet, these methods suffer from an inherent trade-off between the efficiency of photon number dis
Externí odkaz:
https://doaj.org/article/1fde5eb2eeba4fc38f4dd608d7f75d73
Autor:
Dong, Shuyu, Sebag, Michèle, Uemura, Kento, Fujii, Akito, Chang, Shuang, Koyanagi, Yusuke, Maruhashi, Koji
This paper presents a novel approach to causal discovery through a divide-and-conquer framework. By decomposing the problem into smaller subproblems defined on Markov blankets, the proposed DCDILP method first explores in parallel the local causal gr
Externí odkaz:
http://arxiv.org/abs/2406.10481
Autor:
Dong, Shuyu, Uemura, Kento, Fujii, Akito, Chang, Shuang, Koyanagi, Yusuke, Maruhashi, Koji, Sebag, Michèle
Learning causal structures from observational data is a fundamental problem facing important computational challenges when the number of variables is large. In the context of linear structural equation models (SEMs), this paper focuses on learning ca
Externí odkaz:
http://arxiv.org/abs/2211.14221
Common methods to achieve photon number resolution rely on fast on-off single-photon detectors in conjunction with temporal or spatial mode multiplexing. Yet, these methods suffer from an inherent trade-off between the efficiency of photon number dis
Externí odkaz:
http://arxiv.org/abs/2210.16653
Autor:
Dong, Shuyu, Sebag, Michèle
Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling. The NoTears approach of (Zheng et al., 2018), through a differentiable function involving the matrix exponential trace $\math
Externí odkaz:
http://arxiv.org/abs/2204.04644
We study a type of Riemannian gradient descent (RGD) algorithm, designed through Riemannian preconditioning, for optimization on $\mathcal{M}_k^{m\times n}$ -- the set of $m\times n$ real matrices with a fixed rank $k$. Our analysis is based on a quo
Externí odkaz:
http://arxiv.org/abs/2203.06765
Autor:
Zheng, Yiyi, Zhang, Tian, Lee, Pui-Kit, Duan, Qiaohui, Li, Xin, Dong, Shuyu, Tan, Tian, Wang, Yao, Yu, Denis Y.W.
Publikováno v:
In Electrochimica Acta 1 October 2024 500
Publikováno v:
SIAM Journal on Matrix Analysis and Applications, 43-2 (2022), 840-866
We propose new Riemannian preconditioned algorithms for low-rank tensor completion via the polyadic decomposition of a tensor. These algorithms exploit a non-Euclidean metric on the product space of the factor matrices of the low-rank tensor in the p
Externí odkaz:
http://arxiv.org/abs/2101.11108
Publikováno v:
In Journal of Power Sources 1 February 2024 592
We consider a Canonical Polyadic (CP) decomposition approach to low-rank tensor completion (LRTC) by incorporating external pairwise similarity relations through graph Laplacian regularization on the CP factor matrices. The usage of graph regularizat
Externí odkaz:
http://arxiv.org/abs/2008.12876