38 skills found · Page 1 of 2
rballester / TntorchTensor Network Learning with PyTorch
neurostatslab / TensortoolsA very simple and barebones tensor decomposition library for CP decomposition a.k.a. PARAFAC a.k.a. TCA
ruihangdu / Decompose CNNCP and Tucker decomposition for Convolutional Neural Networks
musco-ai / Musco PytorchMUSCO: MUlti-Stage COmpression of neural networks
Einsums / EinsumsProvides compile-time contraction pattern analysis to determine optimal tensor operation to perform.
mohammadbashiri / Tensor Decomposition In PythonA short tutorial on implementing Canonical Polyadic (CP) tensor decomposition in Python
ebigelow / Tf DecomposeTensor decomposition implemented in TensorFlow
sandialabs / PyttbPython Tensor Toolbox
hiroyuki-kasai / OLSTECOnLine Low-rank Subspace tracking by TEnsor CP Decomposition in Matlab: Version 1.0.1
vadim-v-lebedev / Cp DecompositionNo description available
thanhtbt / Tensor Tracking Survey[IEEE TKDE 2023] A list of up-to-date papers on streaming tensor decomposition, tensor tracking, dynamic tensor analysis
erichson / RTensorRandomized Tensor Decompositions
arkmagus / Tensor RnnAn implementation of various tensor-based decomposition for NN & RNN parameters
lanl / PyCP APRCP-APR Tensor Decomposition with PyTorch backend. pyCP_APR can perform non-negative Poisson Tensor Factorization on GPU, and includes an interface for anomaly detection using the extracted latent patterns.
FurongHuang / ConvDicLearnTensorFactorTensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning. Model parameters are estimated via CP decomposition of the observed higher order input moments. However, in many domains, additional invariances such as shift invariances exist, enforced via models such as convolutional dictionary learning. In this paper, we develop novel tensor decomposition algorithms for parameter estimation of convolutional models. Our algorithm is based on the popular alternating least squares method, but with efficient projections onto the space of stacked circulant matrices. Our method is embarrassingly parallel and consists of simple operations such as fast Fourier transforms and matrix multiplications. Our algorithm converges to the dictionary much faster and more accurately compared to the alternating minimization over filters and activation maps.
DMLab-Tensor / SliceNStitchSliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams (ICDE'21)
rlminste / CP ALS QRThis repository contains MATLAB codes for CP tensor decompositions that use the more stable QR decomposition for problems with ill-conditioning
thanhtbt / ROLCP[IEEE ICASSP 2021] "A fast randomized adaptive CP decomposition for streaming tensors". In 46th IEEE International Conference on Acoustics, Speech, & Signal Processing, 2021.
yipengliu / Tensor Rank Cnntensor rank learning in CP decomposition via convolutional neural network
TU-Ilmenau-CRL / SECSIDemo implementation for the SEmi-algebraic framework for the approximate CP decompositions via SImultaneous Matrix Diagonalizations (SECSI).
