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Deep Convolutional Neural Network Compression Method: Tensor Ring Decomposition with Variational Bayesian Approach
Due to deep neural networks (DNNs) a large number of parameters, DNNs increase the demand for computing and storage during training, reasoning and...
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Low-Rank Tensor Decomposition
Infrared small target detection is a research hotspot in computer vision technology that plays an important role in infrared early warning systems....
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Fast hypergraph regularized nonnegative tensor ring decomposition based on low-rank approximation
Tensor ring (TR) decomposition is a highly effective tool for obtaining the low-rank character of multi-way data. Recently, nonnegative tensor ring...
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Towards efficient and accurate approximation: tensor decomposition based on randomized block Krylov iteration
Tensor decomposition methods are inefficient when dealing with low-rank approximation of large-scale data. Randomized tensor decomposition has...
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Application of Tensor Network Formalism for Processing Tensor Data
Next-generation mobility services require a huge amount of data with multiple attributes. This data is stored as a multi-dimensional array called a...
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Scalable Bayesian Tensor Ring Factorization for Multiway Data Analysis
Tensor decompositions play a crucial role in numerous applications related to multi-way data analysis. By employing a Bayesian framework with...
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Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space
In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its...
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Image inpainting algorithm based on tensor decomposition and weighted nuclear norm
For a damaged image, recovering an image with missing entire rows or columns is a challenging problem arising in many real applications, such as...
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Two Birds with One Stone: A Link Prediction Model for Knowledge Hypergraph Based on Fully-Connected Tensor Decomposition
Knowledge hypergraph link prediction aims to predict missing relationships in knowledge hypergraphs and is one of the effective methods for graph...
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Multi-type clustering using regularized tensor decomposition
Geospatial analytics increasingly rely on data fusion methods to extract patterns from data; however robust results are difficult to achieve because...
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WAE-TLDN: self-supervised fusion for multimodal medical images via a weighted autoencoder and a tensor low-rank decomposition network
Multimodal medical image fusion (MMIF) integrates the advantages of multiple source images to assist clinical diagnosis. Existing image fusion...
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Bayesian Tensor Completion and Decomposition with Automatic CP Rank Determination Using MGP Shrinkage Prior
Tensor completion, which completes high-dimensional data with missing entries, has many applications, such as recommender systems and image...
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Theories, algorithms and applications in tensor learning
Due to the accelerated development and popularization of Internet, mobile Internet, and Internet of Things and the breakthrough of storage and...
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Projected Entangled Pair State Tensor Network for Colour Image and Video Completion
Tensor decompositions, such as the CP, Tucker, tensor train, and tensor ring decomposition, have yielded many promising results in science and...
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Convolutional Neural Network Compression via Tensor-Train Decomposition on Permuted Weight Tensor with Automatic Rank Determination
Convolutional neural networks (CNNs) are among the most commonly investigated models in computer vision. Deep CNNs yield high computational...
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TendiffPure: a convolutional tensor-train denoising diffusion model for purification
Diffusion models are effective purification methods, where the noises or adversarial attacks are removed using generative approaches before...
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A High-Order Tensor Completion Algorithm Based on Fully-Connected Tensor Network Weighted Optimization
Tensor completion aims at recovering missing data, and it is one of the popular concerns in deep learning and signal processing. Among the...
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Adaptive graph regularized non-negative Tucker decomposition for multiway dimensionality reduction
Non-negative Tucker decomposition (NTD) is a powerful tool for data representation to capture rich internal structure information from non-negative...
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Improvement of robust tensor principal component analysis based on generalized nonconvex approach
The problem of nonconvex robust tensor principal component analysis consists of recovering the low-rank and sparse part from a tensor corrupted by...
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A general multi-factor norm based low-rank tensor completion framework
Low-rank tensor completion aims to recover the missing entries of the tensor from its partially observed data by using the low-rank property of the...
