1 code implementation • 19 Oct 2021 • Tenghui Li, Guoxu Zhou, Yuning Qiu, Qibin Zhao
We make an attempt to understanding convolutional neural network by exploring the relationship between (deep) convolutional neural networks and Volterra convolutions.
no code implementations • 20 Nov 2017 • Yuning Qiu, Guoxu Zhou, Kan Xie
Nonnegative Matrix Factorization (NMF) is a widely used technique for data representation.
no code implementations • 14 Oct 2019 • Jinshi Yu, Weijun Sun, Yuning Qiu, Shengli Xie
In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding scheme.
no code implementations • 27 Feb 2022 • Zhenhao Huang, Yuning Qiu, Xinqi Chen, Weijun Sun, Guoxu Zhou
Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observation with outlier corruption.
no code implementations • 15 Mar 2022 • Yuning Qiu, Teruhisa Misu, Carlos Busso
The experimental results reveal that recordings annotated with events that are likely to be anomalous, such as avoiding on-road pedestrians and traffic rule violations, have higher anomaly scores than recordings without any event annotation.
no code implementations • 14 Mar 2022 • Yuning Qiu, Guoxu Zhou, Qibin Zhao, Shengli Xie
Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.
no code implementations • 4 Apr 2022 • Peilin Yang, Yonghui Huang, Yuning Qiu, Weijun Sun, Guoxu Zhou
The algorithm performs a composition of the completed tensor by initialising the factors from the FCTN decomposition.