303 citations · 885 across the 16 of their papers we have counts for
16 papers
Efficient Tensor Robust PCA under Hybrid Model of Tucker and Tensor Train
Yuning Qiu, Guoxu Zhou, Zhenhao Huang +2
Tensor robust principal component analysis (TRPCA) is a fundamental model in machine learning and computer vision. Recently, tensor train (TT) decomposition has been verified effec…
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
Xumin Huang, Peichun Li, Rong Yu +3
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the applicatio…
Robust Output Regulation and Reinforcement Learning-based Output Tracking Design for Unknown Linear Discrete-Time Systems
Ci Chen, Lihua Xie, Yi Jiang +2
In this paper, we investigate the optimal output tracking problem for linear discrete-time systems with unknown dynamics using reinforcement learning and robust output regulation t…
Domain Confusion with Self Ensembling for Unsupervised Adaptation
Jiawei Wang, Zhaoshui He, Chengjian Feng +4
Data collection and annotation are time-consuming in machine learning, expecially for large scale problem. A common approach for this problem is to transfer knowledge from a relate…
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling
Chao Li, Mohammad Emtiyaz Khan, Zhun Sun +4
Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis. The numerical defects of TD methods, however…
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Jinshi Yu, Guoxu Zhou, Andrzej Cichocki +1
Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfac…