Block tensor train decomposition for missing data estimation
Sign Up to like & getrecommendations! Published in 2018 at "Statistical Papers"
DOI: 10.1007/s00362-018-1043-8
Abstract: We propose a method for imputation of missing values in large scale matrix data based on a low-rank tensor approximation technique called the block tensor train (BTT) decomposition. Given sparsely observed data points, the proposed… read more here.
Keywords: tensor train; tensor; block tensor; method ... See more keywords
Randomized algorithms for the approximations of Tucker and the tensor train decompositions
Sign Up to like & getrecommendations! Published in 2019 at "Advances in Computational Mathematics"
DOI: 10.1007/s10444-018-9622-8
Abstract: Randomized algorithms provide a powerful tool for scientific computing. Compared with standard deterministic algorithms, randomized algorithms are often faster and robust. The main purpose of this paper is to design adaptive randomized algorithms for computing… read more here.
Keywords: tensor train; adaptive randomized; tensor; algorithms approximations ... See more keywords
A continuous analogue of the tensor-train decomposition
Sign Up to like & getrecommendations! Published in 2019 at "Computer Methods in Applied Mechanics and Engineering"
DOI: 10.1016/j.cma.2018.12.015
Abstract: We describe a new function approximation framework based on a continuous extension of the tensor-train decomposition. The approximation, termed a function-train (FT), results in a tensor-train structure whose cores are univariate functions. An advantage of… read more here.
Keywords: tensor train; approximation; train decomposition;
Functional Tensor-Train Chebyshev Method for Multidimensional Quantum Dynamics Simulations.
Sign Up to like & getrecommendations! Published in 2021 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.1c00941
Abstract: Methods for efficient simulations of multidimensional quantum dynamics are essential for theoretical studies of chemical systems where quantum effects are important, such as those involving rearrangements of protons or electronic configurations. Here, we introduce the… read more here.
Keywords: quantum; quantum dynamics; multidimensional quantum; tensor train ... See more keywords
Tensor-Train Thermo-Field Memory Kernels for Generalized Quantum Master Equations.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.2c00892
Abstract: The generalized quantum master equation (GQME) approach provides a rigorous framework for deriving the exact equation of motion for any subset of electronic reduced density matrix elements (e.g., the diagonal elements). In the context of… read more here.
Keywords: tensor train; train thermo; memory; memory kernels ... See more keywords
Tensor Train Optimization for Conformational Sampling of Organic Molecules.
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.4c01275
Abstract: Exploring the conformational space of molecules remains a challenge of fundamental importance to quantum chemistry: identification of relevant conformers at ambient conditions enables predictive simulations of almost arbitrary properties. Here, we propose a novel approach,… read more here.
Keywords: train optimization; conformational sampling; organic molecules; tensor train ... See more keywords
Tensor train regression with convex regularization
Sign Up to like & getrecommendations! Published in 2025 at "Statistics"
DOI: 10.1080/02331888.2025.2482070
Abstract: Tensor regression has received increasing attention in recent years since more and more data sets are naturally represented in tensor structures. In the regression setting, most existing works are concerned with tensors in CP or… read more here.
Keywords: train regression; tensor train; tensor; convex regularization ... See more keywords
Inchworm tensor train hybridization expansion quantum impurity solver
Sign Up to like & getrecommendations! Published in 2025 at "Physical Review B"
DOI: 10.1103/yt8p-vr1v
Abstract: The investigation of quantum impurity models plays a crucial role in condensed matter physics because of their wide-ranging applications, such as embedding theories and transport problems. Traditional methods often fall short of producing accurate results… read more here.
Keywords: quantum impurity; impurity; tensor train;
Data Driven Prognosis of Fracture Dynamics Using Tensor Train and Gaussian Process Regression
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3042830
Abstract: Accurate prediction of the mechanical failure of structural components plays an important role in the design of engineering structures. However, the fracture process is challenging to model numerically due to the existence of an elastic… read more here.
Keywords: process; fracture; model; process regression ... See more keywords
Application of Tensor Train Decomposition in S2VT Model for Sign Language Recognition
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3059660
Abstract: Sign language recognition is a conversion of sign language into text or speech, bridging the communication between the hearing and society. Recently, sequence-to-sequence video to text (S2VT) models has been employed in the field of… read more here.
Keywords: tensor train; s2vt; tensor; sign language ... See more keywords
TT-MLP: Tensor Train Decomposition on Deep MLPs
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3240784
Abstract: Deep multilayer perceptrons (MLPs) have achieved promising performance on computer vision tasks. Deep MLPs consist solely of fully-connected layers as the conventional MLPs do but adopt more sophisticated network architectures based on mixer layers composed… read more here.
Keywords: tensor train; train decomposition; deep mlps; train ... See more keywords