Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Wu, Yikai"'
Autor:
SENSEI Collaboration, Adari, Prakruth, Bloch, Itay M., Botti, Ana M., Cababie, Mariano, Cancelo, Gustavo, Cervantes-Vergara, Brenda A., Crisler, Michael, Daal, Miguel, Desai, Ansh, Drlica-Wagner, Alex, Essig, Rouven, Estrada, Juan, Etzion, Erez, Moroni, Guillermo Fernandez, Holland, Stephen E., Kehat, Yonatan, Korn, Yaron, Lawson, Ian, Luoma, Steffon, Orly, Aviv, Perez, Santiago E., Rodrigues, Dario, Saffold, Nathan A., Scorza, Silvia, Singal, Aman, Sofo-Haro, Miguel, Stefanazzi, Leandro, Stifter, Kelly, Tiffenberg, Javier, Uemura, Sho, Villalpando, Edgar Marrufo, Volansky, Tomer, Wu, Yikai, Yu, Tien-Tien, Emken, Timon, Xu, Hailin
We present the first results from a dark matter search using six Skipper-CCDs in the SENSEI detector operating at SNOLAB. With an exposure of 534.9 gram-days from well-performing sensors, we select events containing 2 to 10 electron-hole pairs. After
Externí odkaz:
http://arxiv.org/abs/2312.13342
Publikováno v:
Bulletin of the Chinese Academy of Sciences / Chung-kuo ko Hsueh Yuan Yuan Kan. 2024, Vol. 39 Issue 8, p1375-1388. 14p.
Publikováno v:
Proceedings of Thirty Fifth Conference on Learning Theory (COLT), PMLR 178:3547-3588, 2022
Consider the following optimization problem: Given $n \times n$ matrices $A$ and $\Lambda$, maximize $\langle A, U\Lambda U^*\rangle$ where $U$ varies over the unitary group $\mathrm{U}(n)$. This problem seeks to approximate $A$ by a matrix whose spe
Externí odkaz:
http://arxiv.org/abs/2207.02794
Publikováno v:
In Applied Thermal Engineering 15 October 2024 255
Publikováno v:
In Applied Thermal Engineering 1 October 2024 254
Autor:
Dong, Shichang, Jin, Yu, Gong, Shengjie, Wu, Yikai, Li, Wei, Guo, Qiang, Xiong, Zhenqin, Yuan, Yidan
Publikováno v:
In Applied Thermal Engineering 1 March 2024 240
Publikováno v:
In Soil Dynamics and Earthquake Engineering February 2024 177
Publikováno v:
PVLDB, 14(10): 1805-1817, 2021
Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and policy making problems. These organizations are also legally required to
Externí odkaz:
http://arxiv.org/abs/2011.01192
Hessian captures important properties of the deep neural network loss landscape. Previous works have observed low rank structure in the Hessians of neural networks. In this paper, we propose a decoupling conjecture that decomposes the layer-wise Hess
Externí odkaz:
http://arxiv.org/abs/2010.04261
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