A Julia library for efficient tensor computations and tensor network calculations
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Updated
May 26, 2024 - Julia
A Julia library for efficient tensor computations and tensor network calculations
Tensor network simulations for finite temperature, open quantum system dynamics
Tensor Train Toolbox
Tensor Network Learning with PyTorch
Gradient-free optimization method for the multidimensional arrays and discretized multivariate functions based on the tensor train (TT) format.
🥭 MANGO: Maximization of neural Activation via Non-Gradient Optimization
Parabolic PDE resolution with Tensor Networks using Backward-Forward Stochastic Differential Equations
[IEEE TKDE 2023] A list of up-to-date papers on streaming tensor decomposition, tensor tracking, dynamic tensor analysis
A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays
Visualization of Tensor Decompositions
Tensor-Train decomposition in pytorch
[SP 2024] A Novel Recursive Least-Squares Adaptive Method For Streaming Tensor-Train Decomposition With Incomplete Observations. In Elsevier Signal Processing, 2024.
Black-box adversarial attacks on deep neural networks with tensor train (TT) decomposition and PROTES optimizer.
Numerical experiments for Optima-TT method from teneva python package. This method finds items which relate to min and max elements of the tensor in the tensor train (TT) format.
Solver in the low-rank tensor train format with cross approximation approach for the multidimensional Fokker-Planck equation
Gradient-free optimization method for multivariable functions based on the low rank tensor train (TT) format and maximal-volume principle.
PRobability Optimizer with TEnsor Sampling (PROTES) is an optimization algorithm based on tensor train decomposition.
[EUSIPCO 2022] "Robust Tensor Tracking With Missing Data Under Tensor-Train Format". In 30th European Signal Processing Conference, 2022.
[EUSIPCO 2020] "Adaptive Algorithms for Tensor Train Decomposition of Streaming Tensors". In 28th European Signal Processing Conference, 2020.
A fully tensorized recurrent neural network using tensor-train decomposition
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