24 citations · 54 across the 15 of their papers we have counts for
6 papers · 1 filter
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality
Xinyu Zhou, Chang Liu, Jun Zhao
The Metaverse has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with t…
Time Minimization in Hierarchical Federated Learning
Chang Liu, Terence Jie Chua, Jun Zhao
Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a centr…
Robust Bayesian Inference for Moving Horizon Estimation
Wenhan Cao, Chang Liu, Zhiqian Lan +3
The accuracy of moving horizon estimation (MHE) suffers significantly in the presence of measurement outliers. Existing methods address this issue by treating measurements leading…
The path integral formula for the stochastic evolutionary game dynamics in the Moran process
Chao Wang
The Moran process is one of an basic mathematical structure in the evolutionary game theory. In this work, we introduce the formulation of the path integral approach for evolutiona…
Open-set Text Recognition via Character-Context Decoupling
Chang Liu, Chun Yang, Xu-Cheng Yin
The open-set text recognition task is an emerging challenge that requires an extra capability to cognize novel characters during evaluation. We argue that a major cause of the limi…
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Wenhan Cao, Jingliang Duan, Shengbo Eben Li +3
This paper proposes a primal-dual framework to learn a stable estimator for linear constrained estimation problems leveraging the moving horizon approach. To avoid the online compu…