Zobrazeno 1 - 10
of 21 711
pro vyhledávání: '"WEN Wei"'
Autor:
Shahidi, Freydoon
Publikováno v:
Proceedings of the Simons symposium at Elmau, Germany in April 2016, Springer
Any generalization of the method of Godement-Jacquet on principal L-functions for GL(n) to other groups as perceived by Braverman-Kazhdan and Ngo requires a Fourier transform on a space of Schwartz functions. In the case of standard L-functions for c
Externí odkaz:
http://arxiv.org/abs/1710.06841
Autor:
Zeng, Zhichen, Liu, Xiaolong, Hang, Mengyue, Liu, Xiaoyi, Zhou, Qinghai, Yang, Chaofei, Liu, Yiqun, Ruan, Yichen, Chen, Laming, Chen, Yuxin, Hao, Yujia, Xu, Jiaqi, Nie, Jade, Liu, Xi, Zhang, Buyun, Wen, Wei, Yuan, Siyang, Wang, Kai, Chen, Wen-Yen, Han, Yiping, Li, Huayu, Yang, Chunzhi, Long, Bo, Yu, Philip S., Tong, Hanghang, Yang, Jiyan
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interest
Externí odkaz:
http://arxiv.org/abs/2411.09852
Autor:
Shi, Xihang, Lee, Wen Wei, Karnieli, Aviv, Lohse, Leon Merten, Gorlach, Alexey, Wong, Lee Wei Wesley, Saldit, Tim, Fan, Shanhui, Kaminer, Ido, Wong, Liang Jie
Rapid progress in precision nanofabrication and atomic design over the past 50 years has ushered in a succession of transformative eras for molding the generation and flow of light. The use of nanoscale and atomic features to design light sources and
Externí odkaz:
http://arxiv.org/abs/2411.09019
Autor:
Zhang, Tunhou, Cheng, Dehua, He, Yuchen, Chen, Zhengxing, Dai, Xiaoliang, Xiong, Liang, Liu, Yudong, Cheng, Feng, Cao, Yufan, Yan, Feng, Li, Hai, Chen, Yiran, Wen, Wei
Publikováno v:
ACM Transactions on Recommender Systems (TORS) 2024
The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization deman
Externí odkaz:
http://arxiv.org/abs/2411.07569
Autor:
Li, Wen-Wei
Let $\mathrm{Mp}(2n)$ be the metaplectic group of rank $n$ over a local field $F$ of characteristic zero. In this note, we determine the behavior of endoscopic transfer for $\mathrm{Mp}(2n)$ under variation of additive characters of $F$. The argument
Externí odkaz:
http://arxiv.org/abs/2411.03091
We analyse the entanglement structure of states generated by random constant-depth two-dimensional quantum circuits, followed by projective measurements of a subset of sites. By deriving a rigorous lower bound on the average entanglement entropy of s
Externí odkaz:
http://arxiv.org/abs/2410.23248
Autor:
Li, Wen-Wei
For metaplectic groups over a local field of characteristic zero, we define the Arthur packet attached to any Arthur parameter $\psi$ as a multi-set of unitary genuine irreducible representations, characterized by endoscopic character relations. Over
Externí odkaz:
http://arxiv.org/abs/2410.13606
Autor:
Tan WS; Department of Urology, University of Texas MD Anderson Cancer Center, Houston, TX, USA., O'Donnell M; Department of Urology, University of Iowa, Iowa City, IA, USA., Li R; Department of Urology, Moffitt Cancer Center, Tampa, FL, USA., Kamat AM; Department of Urology, University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: akamat@mdanderson.org., Packiam VT; Department of Urology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
Publikováno v:
European urology oncology [Eur Urol Oncol] 2023 Oct 26. Date of Electronic Publication: 2023 Oct 26.
For handling intercurrent events in clinical trials, one of the strategies outlined in the ICH E9(R1) addendum targets the hypothetical scenario of non-occurrence of the intercurrent event. While this strategy is often implemented by setting data aft
Externí odkaz:
http://arxiv.org/abs/2409.10943
Scaling up deep learning models has been proven effective to improve intelligence of machine learning (ML) models, especially for industry recommendation models and large language models. The co-design of large distributed ML systems and algorithms (
Externí odkaz:
http://arxiv.org/abs/2409.04585