Search Results for author:
Found 7 papers, 1 papers with code
A high-order tensor completion algorithm based on Fully-Connected Tensor Network weighted optimization
The algorithm performs a composition of the completed tensor by initialising the factors from the FCTN decomposition.
Driving Anomaly Detection Using Conditional Generative Adversarial Network
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.
Noisy Tensor Completion via Low-rank Tensor Ring
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.
Bayesian Robust Tensor Ring Model for Incomplete Multiway Data
Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observation with outlier corruption.
Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective
We make an attempt to understanding convolutional neural network by exploring the relationship between (deep) convolutional neural networks and Volterra convolutions.
An Efficient Tensor Completion Method via New Latent Nuclear Norm
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.
Deep Approximately Orthogonal Nonnegative Matrix Factorization for Clustering
Nonnegative Matrix Factorization (NMF) is a widely used technique for data representation.
