tensor decomposition - Robuta Search

https://deepai.org/publication/heterogeneous-tensor-decomposition-for-clustering-via-manifold-optimization Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization | DeepAI Apr 7, 2015 - 04/07/15 - Tensors or multiarray data are generalizations of matrices. Tensor clustering has become a very important research topic due to th... tensor decomposition heterogeneous clustering via manifold https://deepai.org/publication/generalized-canonical-polyadic-tensor-decomposition Generalized Canonical Polyadic Tensor Decomposition | DeepAI Aug 22, 2018 - 08/22/18 - Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network an... tensor decomposition generalized canonical polyadic deepai https://arxiv.org/abs/2505.23046 [2505.23046] Revisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence Abstract page for arXiv paper 2505.23046: Revisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence tensor decomposition 2505 23046 revisit cp https://www.mdpi.com/1424-8220/23/14/6616 TDFusion: When Tensor Decomposition Meets Medical Image Fusion in the Nonsubsampled Shearlet... In this paper, a unified optimization model for medical image fusion based on tensor decomposition and the non-subsampled shearlet transform (NSST) is... tensor decomposition medical image https://www.kth.se/math/kalender/can-chen-homogeneous-polynomial-systems-theory-through-tensor-decomposition-1.1407088?date=2025-06-03&orgdate=2025-03-13&length=1&orglength=294 Can Chen: Homogeneous Polynomial Systems Theory through Tensor Decomposition | KTH homogeneous polynomial systems theory tensor decomposition chen kth https://jmlr.org/papers/v26/25-0134.html High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces cartesian tensor high rank irreducible https://deepai.org/publication/joint-probability-estimation-using-tensor-decomposition-and-dictionaries Joint Probability Estimation Using Tensor Decomposition and Dictionaries | DeepAI Mar 3, 2022 - 03/03/22 - In this work, we study non-parametric estimation of joint probabilities of a given set of discrete and continuous random variables... joint probability tensor decomposition estimation using dictionaries https://openreview.net/forum?id=HlYbQ2D90P Multi-view Graph Condensation via Tensor Decomposition | OpenReview Training Graph Neural Networks (GNNs) on large-scale graphs presents significant computational challenges due to the resources required for their storage and... multi view tensor decomposition graph condensation via https://deepai.org/publication/sparse-logistic-tensor-decomposition-for-binary-data Sparse Logistic Tensor Decomposition for Binary Data | DeepAI Jun 27, 2021 - 06/27/21 - Tensor data are increasingly available in many application domains. We develop several tensor decomposition methods for binary ten... tensor decomposition binary data sparse logistic deepai https://pmc.ncbi.nlm.nih.gov/articles/PMC7904970/ Multi-paradigm fMRI fusion via sparse tensor decomposition in brain functional connectivity study -... Functional magnetic resonance imaging (fMRI) is a powerful technique with the potential to estimate individual variations in behavioral and cognitive traits.... https://deepai.org/publication/fast-and-accurate-low-rank-tensor-completion-methods-based-on-qr-decomposition-and-l-21-norm-minimization Fast and Accurate Low-Rank Tensor Completion Methods Based on QR Decomposition and L_2,1 Norm... Aug 6, 2021 - 08/06/21 - More recently, an Approximate SVD Based on Qatar Riyal (QR) Decomposition (CSVD-QR) method for matrix complete problem is presente... https://openreview.net/forum?id=sBaUZzZXJN&referrer=%5Bthe%20profile%20of%20Jiawei%20Zhao%5D(%2Fprofile%3Fid%3D~Jiawei_Zhao2) Tensor-GaLore: Memory-Efficient Training via Gradient Tensor Decomposition | OpenReview We present Tensor-GaLore, a novel method for efficient training of neural networks with higher-order tensor weights. Many models, particularly those used in... efficient training tensor galore memory via https://github.com/AndreiChertkov/teneva GitHub - AndreiChertkov/teneva: A framework based on the tensor train decomposition for working... A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays - GitHub - AndreiChertkov/teneva: A... https://jmlr.org/papers/v21/18-008.html Tensor Train Decomposition on TensorFlow (T3F) tensor train decomposition https://deepai.org/publication/tucker-l-2e-robust-low-rank-tensor-decomposition-with-the-l-2-criterion Tucker-L_2E: Robust Low-rank Tensor Decomposition with the L_2 Criterion | DeepAI Aug 25, 2022 - 08/25/22 - The growing prevalence of tensor data, or multiway arrays, in science and engineering applications motivates the need for tensor d... https://arxiv.org/abs/2010.08581 [2010.08581] A Sampling-Based Method for Tensor Ring Decomposition Abstract page for arXiv paper 2010.08581: A Sampling-Based Method for Tensor Ring Decomposition a sampling tensor ring 2010 08581 based https://www.amazon.science/publications/e-commerce-abuse-detection-via-semi-supervised-binary-multi-target-tensor-decomposition E-commerce abuse detection via semi-supervised binary multi-target tensor decomposition - Amazon... Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness,... https://www.jmlr.org/papers/v26/24-1229.html Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition https://openreview.net/forum?id=sBaUZzZXJN Tensor-GaLore: Memory-Efficient Training via Gradient Tensor Decomposition | OpenReview We present Tensor-GaLore, a novel method for efficient training of neural networks with higher-order tensor weights. Many models, particularly those used in... efficient training tensor galore memory via https://openreview.net/forum?id=JVwJp5ubTQ Convolutional Hierarchical Deep Learning Neural Networks-Tensor Decomposition (C-HiDeNN-TD): a... A common trend in simulation-driven engineering applications is the ever-increasing size and complexity of the problem, where classical numerical methods... hierarchical deep learning neural networks https://www.ornl.gov/publication/monti-multi-omics-non-negative-tensor-decomposition-framework-gene-level-integrative MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative... Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex... https://deepai.org/publication/estimating-joint-probability-distribution-with-low-rank-tensor-decomposition-radon-transforms-and-dictionaries Estimating Joint Probability Distribution With Low-Rank Tensor Decomposition, Radon Transforms and... Apr 18, 2023 - 04/18/23 - In this paper, we describe a method for estimating the joint probability density from data samples by assuming that the underlying... joint probability