1.4k citations
- RIKENJP127 papers
- The University of TokyoJP69 papers
- Japan Science and Technology AgencyJP30 papers
- Kyoto UniversityJP14 papers
- Systems Research InstitutePL12 papers
- University of TsukubaJP10 papers
- Forschungszentrum JülichDE8 papers
- Kobe City College of TechnologyJP8 papers
- Polish Academy of SciencesPL8 papers
- Yokohama National UniversityJP8 papers
- Centre National de la Recherche ScientifiqueFR7 papers
- Yamaguchi UniversityJP7 papers
13 papers · 1 filter
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
A. Cichocki, A-H. Phan, Q. Zhao +4
Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-ord…
Learning Efficient Tensor Representations with Ring Structure Networks
Qibin Zhao, Masashi Sugiyama, Andrzej Cichocki
Tensor train (TT) decomposition is a powerful representation for high-order tensors, which has been successfully applied to various machine learning tasks in recent years. However,…
Tensor Networks for Latent Variable Analysis. Part I: Algorithms for Tensor Train Decomposition
Anh-Huy Phan, Andrzej Cichocki, Andre Uschmajew +3
Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general ten…
Tensor Ring Decomposition
Qibin Zhao, Guoxu Zhou, Shengli Xie +2
Tensor networks have in recent years emerged as the powerful tools for solving the large-scale optimization problems. One of the most popular tensor network is tensor train (TT) de…
Tensor Deflation for CANDECOMP/PARAFAC. Part 3: Rank Splitting
Anh-Huy Phan, Petr Tichavsky, Andrzej Cichocki
CANDECOMP/PARAFAC (CPD) approximates multiway data by sum of rank-1 tensors. Our recent study has presented a method to rank-1 tensor deflation, i.e. sequential extraction of the r…
Very Large-Scale Singular Value Decomposition Using Tensor Train Networks
Namgil Lee, Andrzej Cichocki
We propose new algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format…