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Found 5 papers, 1 papers with code
Matrix Product State for Higher-Order Tensor Compression and Classification
This paper introduces matrix product state (MPS) decomposition as a new and systematic method to compress multidimensional data represented by higher-order tensors.
Concatenated image completion via tensor augmentation and completion
The proposed framework performs image completion by concatenating copies of a single image that has missing entries into a third-order tensor, applying a dimensionality augmentation technique to the tensor, utilizing a tensor completion algorithm for recovering its missing entries, and finally extracting the recovered image from the tensor.
Efficient tensor completion for color image and video recovery: Low-rank tensor train
The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme.
Numerical Analysis Data Structures and Algorithms
The iPEPS algorithm, improved: fast full update and gauge fixing
The infinite Projected Entangled Pair States (iPEPS) algorithm [J. Jordan et al, PRL 101, 250602 (2008)] has become a useful tool in the calculation of ground state properties of 2d quantum lattice systems in the thermodynamic limit.
Strongly Correlated Electrons High Energy Physics - Lattice Quantum Physics
Matrix Product State for Feature Extraction of Higher-Order Tensors
This paper introduces matrix product state (MPS) decomposition as a computational tool for extracting features of multidimensional data represented by higher-order tensors.
