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07/03/2023
Semi-supervised multi-view concept decomposition
Concept Factorization (CF), as a novel paradigm of representation learni...
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05/24/2023
SVDinsTN: An Integrated Method for Tensor Network Representation with Efficient Structure Search
Tensor network (TN) representation is a powerful technique for data anal...
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04/25/2023
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
Tensor network (TN) is a powerful framework in machine learning, but sel...
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03/01/2023
Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks
Achieving efficient and robust multi-channel data learning is a challeng...
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11/27/2022
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration
Efficient and accurate low-rank approximation (LRA) methods are of great...
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10/07/2022
Latent Matrices for Tensor Network Decomposition and to Tensor Completion
The prevalent fully-connected tensor network (FCTN) has achieved excelle...
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06/14/2022
Permutation Search of Tensor Network Structures via Local Sampling
Recent works put much effort into tensor network structure search (TN-SS...
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06/02/2022
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Electroencephalography (EEG) provides access to neuronal dynamics non-in...
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03/14/2022
Noisy Tensor Completion via Low-rank Tensor Ring
Tensor completion is a fundamental tool for incomplete data analysis, wh...
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01/03/2022
Multi-view Data Classification with a Label-driven Auto-weighted Strategy
Distinguishing the importance of views has proven to be quite helpful fo...
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12/20/2021
Efficient Tensor Robust PCA under Hybrid Model of Tucker and Tensor Train
Tensor robust principal component analysis (TRPCA) is a fundamental mode...
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10/19/2021
Understanding Convolutional Neural Networks from Theoretical Perspective via Volterra Convolution
This study proposes a general and unified perspective of convolutional n...
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09/06/2021
Fast Hypergraph Regularized Nonnegative Tensor Ring Factorization Based on Low-Rank Approximation
For the high dimensional data representation, nonnegative tensor ring (N...
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03/02/2021
On the Memory Mechanism of Tensor-Power Recurrent Models
Tensor-power (TP) recurrent model is a family of non-linear dynamical sy...
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11/28/2020
Learning from Incomplete Data by Simultaneous Training of Neural Networks and Sparse Coding
Handling correctly incomplete datasets in machine learning is a fundamen...
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10/24/2020
Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration
Non-local low-rank tensor approximation has been developed as a state-of...
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10/12/2020
Graph Regularized Nonnegative Tensor Ring Decomposition for Multiway Representation Learning
Tensor ring (TR) decomposition is a powerful tool for exploiting the low...
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01/29/2020
H-OWAN: Multi-distorted Image Restoration with Tensor 1x1 Convolution
It is a challenging task to restore images from their variants with comb...
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01/06/2020
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization
Hyperspectral super-resolution (HSR) fuses a low-resolution hyperspectra...
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08/08/2019
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"
Deep image prior (DIP), which utilizes a deep convolutional network (Con...
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07/01/2019
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
There has been an increased interest in multimodal language processing i...
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03/21/2019
Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completion
Tensor ring (TR) decomposition has been successfully used to obtain the ...
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03/14/2019
Tucker Tensor Layer in Fully Connected Neural Networks
We introduce the Tucker Tensor Layer (TTL), an alternative to the dense ...
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01/07/2019
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction
Dimensionality reduction is an essential technique for multi-way large-s...
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12/11/2018
Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
Non-local low-rank tensor approximation has been developed as a state-of...
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11/30/2018
AI Neurotechnology for Aging Societies -- Task-load and Dementia EEG Digital Biomarker Development Using Information Geometry Machine Learning Methods
Dementia and especially Alzheimer's disease (AD) are the most common cau...
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10/31/2018
Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling
Although the convolutional neural networks (CNNs) have become popular fo...
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09/07/2018
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion
In tensor completion tasks, the traditional low-rank tensor decompositio...
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07/03/2018
Higher-dimension Tensor Completion via Low-rank Tensor Ring Decomposition
The problem of incomplete data is common in signal processing and machin...
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06/13/2018
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach
One of the current issues in Brain-Computer Interface is how to deal wit...
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05/22/2018
Rank Minimization on Tensor Ring: A New Paradigm in Scalable Tensor Decomposition and Completion
In low-rank tensor completion tasks, due to the underlying multiple larg...
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05/22/2018
Low-Rank Tensor Decomposition via Multiple Reshaping and Reordering Operations
Tensor decomposition has been widely applied to find low-rank representa...
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11/21/2017
A generative adversarial framework for positive-unlabeled classification
In this work, we consider the task of classifying the binary positive-un...
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11/07/2017
High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization
In this paper, we aim at the problem of tensor data completion. Tensor-t...
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10/30/2017
Tensorizing Generative Adversarial Nets
Generative Adversarial Network (GAN) and its variants demonstrate state-...
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09/08/2017
Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition
In this paper, we aim at the completion problem of high order tensor dat...
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05/20/2017
Learning Efficient Tensor Representations with Ring Structure Networks
Tensor train (TT) decomposition is a powerful representation for high-or...
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06/17/2016
Tensor Ring Decomposition
Tensor networks have in recent years emerged as the powerful tools for s...
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05/10/2015
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion
Tucker decomposition is the cornerstone of modern machine learning on te...
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10/09/2014
Bayesian Robust Tensor Factorization for Incomplete Multiway Data
We propose a generative model for robust tensor factorization in the pre...
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04/17/2014
Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
Nonnegative Tucker decomposition (NTD) is a powerful tool for the extrac...
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01/25/2014
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powe...
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07/05/2012