no code implementations • 11 Apr 2023 • Tierui Gong, Li Wei, Chongwen Huang, Zhijia Yang, Jiguang He, Mérouane Debbah, Chau Yuen
Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits.
no code implementations • 15 Mar 2023 • Tierui Gong, Li Wei, Zhijia Yang, Mérouane Debbah, Chau Yuen
Holographic multiple-input multiple-output (H-MIMO) is considered as one of the most promising technologies to enable future wireless communications in supporting the expected extreme requirements, such as high energy and spectral efficiency.
1 code implementation • 17 Feb 2023 • Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi
Recommender systems play an important role in many content platforms.
no code implementations • 2 Dec 2022 • Tierui Gong, Panagiotis Gavriilidis, Ran Ji, Chongwen Huang, George C. Alexandropoulos, Li Wei, Zhaoyang Zhang, Mérouane Debbah, H. Vincent Poor, Chau Yuen
In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems.
no code implementations • 14 Oct 2022 • Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nicholas Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel
We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations.
no code implementations • 20 May 2021 • Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel
In this work we study the problem of measuring the fairness of a machine learning model under noisy information.
no code implementations • 22 Jan 2021 • Chongwen Huang, Zhaohui Yang, George C. Alexandropoulos, Kai Xiong, Li Wei, Chau Yuen, Zhaoyang Zhang, Merouane Debbah
We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss.
1 code implementation • 20 Nov 2020 • Wu Kehe, Chen Zuge, Zhang Xiaoliang, Li Wei
In this paper we proposed an object confidence task for this problem, and it shares features with classification task.
no code implementations • 23-25 Octorber 2020 • Xin Ru, Li Wei, and Youyun Xu*
Reliable channel estimation is a crucial task for orthogonal frequency division multiplexing (OFDM) systems to achieve high data rate.
no code implementations • 20 Sep 2020 • Chongwen Huang, Zhaohui Yang, George C. Alexandropoulos, Kai Xiong, Li Wei, Chau Yuen, Zhaoyang Zhang
Wireless communication in the TeraHertz band (0. 1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems.
no code implementations • 4 Aug 2020 • Li Wei, Chongwen Huang, George C. Alexandropoulos, Chau Yuen, Zhaoyang Zhang, Mérouane Debbah
We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station.
no code implementations • 23 Jan 2020 • Ya-guan Qian, Xi-Ming Zhang, Wassim Swaileh, Li Wei, Bin Wang, Jian-hai Chen, Wu-jie Zhou, Jing-sheng Lei
Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples. Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes perturbation. Therefore, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point problem. The method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples.
no code implementations • ACM Conference on Recommender Systems 2019 • Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Ajit Kumthekar, Zhe Zhao, Li Wei, Ed Chi
However, batch loss is subject to sampling bias which could severely restrict model performance, particularly in the case of power-law distribution.
no code implementations • RecSys 2019 • Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi
In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.
no code implementations • 2 Mar 2019 • Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.