no code implementations • 28 Feb 2024 • Xi Wang, Xiaotong Zhao, Juncheng Wang, You Li, Qingjiang Shi
We then propose a joint beamforming and linear stream allocation algorithm, termed as RWMMSE-LSA, which yields closed-form updates with linear stream allocation complexity and is guaranteed to converge to the stationary points of the original joint optimization problem.
no code implementations • 15 Feb 2024 • Jiaxiang Geng, Yanzhao Hou, Xiaofeng Tao, Juncheng Wang, Bing Luo
In this work, we advocate a new independent client sampling strategy to minimize the wall-clock training time of FL, while considering data heterogeneity and system heterogeneity in both communication and computation.
no code implementations • 4 Aug 2023 • Juncheng Wang, Jindong Wang, Xixu Hu, Shujun Wang, Xing Xie
Empirical risk minimization (ERM) is a fundamental machine learning paradigm.
no code implementations • 2 Dec 2022 • Qi Wang, Juncheng Wang, Junyu Gao, Yuan Yuan, Xuelong Li
The mainstream crowd counting methods regress density map and integrate it to obtain counting results.
no code implementations • 12 Jun 2022 • Juncheng Wang, Junyu Gao, Yuan Yuan, Qi Wang
The core reason of intrinsic scale shift being one of the most essential issues in crowd localization is that it is ubiquitous in crowd scenes and makes scale distribution chaotic.
no code implementations • 9 May 2021 • Juncheng Wang, Ben Liang, Min Dong, Gary Boudreau, Hatem Abou-zeid
We consider online convex optimization (OCO) with multi-slot feedback delay, where an agent makes a sequence of online decisions to minimize the accumulation of time-varying convex loss functions, subject to short-term and long-term constraints that are possibly time-varying.