Rethinking Bayesian learning for data analysis: The art of prior and inference in sparsity-aware modeling
Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …
focus of scientific research for over two decades. Among others, supervised sparsity-aware …
Tensor train factorization under noisy and incomplete data with automatic rank estimation
As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition
shows superior performance compared to other tensor decomposition formats. Existing TT …
shows superior performance compared to other tensor decomposition formats. Existing TT …
Multi-domain feature analysis method of MI-EEG signal based on Sparse Regularity Tensor-Train decomposition
Tensor analysis can comprehensively retain multidomain characteristics, which has been
employed in EEG studies. However, existing EEG tensor has large dimension, making it …
employed in EEG studies. However, existing EEG tensor has large dimension, making it …
Striking the right balance: Three-dimensional ocean sound speed field reconstruction using tensor neural networks
S Li, L Cheng, T Zhang, H Zhao, J Li - The Journal of the Acoustical …, 2023 - pubs.aip.org
Accurately reconstructing a three-dimensional (3D) ocean sound speed field (SSF) is
essential for various ocean acoustic applications, but the sparsity and uncertainty of sound …
essential for various ocean acoustic applications, but the sparsity and uncertainty of sound …
Toward interpretable graph tensor convolution neural network for code semantics embedding
Intelligent deep learning-based models have made significant progress for automated
source code semantics embedding, and current research works mainly leverage natural …
source code semantics embedding, and current research works mainly leverage natural …
Low-rank characteristic tensor density estimation part II: Compression and latent density estimation
Learning generative probabilistic models is a core problem in machine learning, which
presents significant challenges due to the curse of dimensionality. This paper proposes a …
presents significant challenges due to the curse of dimensionality. This paper proposes a …
Bayesian low-rank matrix completion with dual-graph embedding: Prior analysis and tuning-free inference
Recently, there is a revival of interest in low-rank matrix completion-based unsupervised
learning through the lens of dual-graph regularization, which has significantly improved the …
learning through the lens of dual-graph regularization, which has significantly improved the …
Bayesian low-rank matrix completion with dual-graph embedding: Prior analysis and tuning-free inference
Recently, there is a revival of interest in low-rank matrix completion-based unsupervised
learning through the lens of dual-graph regularization, which has significantly improved the …
learning through the lens of dual-graph regularization, which has significantly improved the …
Enhanced Tensor Rank Learning in Bayesian PARAFAC2 for Noisy Irregular Tensor Data
To analyze irregular multi-dimensional data with unaligned dimensions, which frequently
appear in real-world signal processing and machine learning tasks, parallel factor analysis 2 …
appear in real-world signal processing and machine learning tasks, parallel factor analysis 2 …
To Fold or Not to Fold: Graph Regularized Tensor Train for Visual Data Completion
Tensor train (TT) representation has achieved tremendous success in visual data
completion tasks, especially when it is combined with tensor folding. However, folding an …
completion tasks, especially when it is combined with tensor folding. However, folding an …