├── README.md └── implementations.md /README.md: -------------------------------------------------------------------------------- 1 | # Tensor Notes 2 | 3 | A catch-all repository to group together notes, thoughts, and various resources on **tensor decomposition**. -------------------------------------------------------------------------------- /implementations.md: -------------------------------------------------------------------------------- 1 | # Tensor Decomposition Implementations 2 | 3 | A growing list of publicly available software to produce tensor decompositions in various formats, sorted by update year. 4 | 5 | | Link | Paper/manual | Updated/released | Language | Tensor Format(s) | Type 6 | |:--:|:--:|:--:|:--:|:--:|:--:| 7 | | [tntorch](https://github.com/rballester/tntorch) | [Notebooks](https://github.com/rballester/tntorch/tree/master/tutorials) | 2018 | Python | TT, Tucker, CP | Compression; learning; sensitivity 8 | | [ttrecipes](https://github.com/rballester/ttrecipes) | - | 2018 | Python | TT | Compression; visualization; sensitivity 9 | | [C3](https://github.com/goroda/Compressed-Continuous-Computation) | - | 2018 | C | TT | Regression/completion; sensitivity; adaptive sampling 10 | | [t3f](https://github.com/Bihaqo/t3f) | [Paper](https://arxiv.org/abs/1801.01928) | 2018 | Python | TT | Regression; deep learning 11 | | [TensorLy](https://tensorly.github.io/stable/index.html) | [Paper](https://arxiv.org/abs/1610.09555) | 2018 | Python | CP; Tucker | Tensor regression; deep learning 12 | | [Polara](https://github.com/Evfro/polara) | [Paper](https://arxiv.org/abs/1607.04228) | 2018 | Python | Tucker | Sparse compression 13 | | [ttpy](https://github.com/oseledets/ttpy) | [Paper 1](http://spring.inm.ras.ru/osel/wp-content/plugins/wp-publications-archive/openfile.php?action=open&file=28), [2](http://www.mat.uniroma2.it/~tvmsscho/papers/Tyrtyshnikov5.pdf) | 2018 | Python | TT | Dense compression; adaptive sampling 14 | | [C++ TT](https://bitbucket.org/dzheltkov/c-tt-library) | - | 2017 | C++ | TT | Dense compression; adaptive sampling 15 | | [Tucker3D](https://github.com/rakhuba/tucker3d) | [Paper](http://arxiv.org/pdf/1402.5649.pdf) | 2017 | Python | Tucker | Adaptive sampling 16 | | [tensorlib](https://github.com/tensorlib/tensorlib) | [Documentation](http://tensorlib.github.io/) | 2017 | Python | CP; Tucker | Dense compression 17 | | [TT-Toolbox](https://github.com/oseledets/TT-Toolbox) | (see above) | 2017 | MATLAB | TT | Dense compression; adaptive sampling 18 | | [Tensor Toolbox](https://pypi.python.org/pypi/TensorToolbox/) | [Paper](http://epubs.siam.org/doi/pdf/10.1137/15M1036919) | 2017 | Python | TT | Dense compression; adaptive sampling 19 | | [Tensorlab](http://www.tensorlab.net/) | [Documentation](http://www.tensorlab.net/doc/) | 2016 | MATLAB | CP; Tucker; BTD; TT; structured variants | Dense and sparse compression (with optional constraints); tensor completion; adaptive sampling 20 | | [scikit-tensor](https://github.com/mnick/scikit-tensor) | - | 2016 | Python | CP; Tucker; RESCAL; DEDICOM; INDSCAL | Dense/sparse compression 21 | | [TTeMPS](http://anchp.epfl.ch/TTeMPS) | [PhD Thesis](https://infoscience.epfl.ch/record/217938) | 2016 | MATLAB | TT | Tensor completion 22 | | [vmmlib-tensor](https://github.com/rballester/vmmlib-tensor) | - | 2016 | C++ | Tucker | Dense compression 23 | | [SPLATT](http://shaden.io/splatt.html) | [Slides](http://www.shaden.io/pdf/2015-Smith-SPLATT-slides.pdf) | 2016 | C++; MATLAB | CP | Sparse compression 24 | | [Tensorbox](http://www.bsp.brain.riken.jp/~phan/index.html#tensorbox) | [Website](http://www.bsp.brain.riken.jp/~phan/) | 2015 | MATLAB | CP, Tucker and variants | Dense compression (with various constraints) 25 | | [tucker_opt](http://www.lair.irb.hr/ikopriva/Data/PhD_Students/mfilipovic/tucker_low_rank_completion_codes.zip) | [Paper](http://www.lair.irb.hr/ikopriva/Data/PhD_Students/mfilipovic/tc_paper.pdf) | 2015 | MATLAB | Tucker | Tensor completion 26 | | [NTFLib](https://github.com/stitchfix/NTFLib) | - | 2015 | Python | NTF | Sparse compression 27 | | [vmmlib](https://github.com/VMML/vmmlib) | [vmmlib classes](https://files.ifi.uzh.ch/vmml/ta_tutorial/vmmlib_ta_classes.pdf) | 2015 | C++ | CP; Tucker | Dense compression 28 | | [MTT](https://github.com/andrewssobral/mtt) | [Slides](http://www.slideshare.net/andrewssobral/matrix-and-tensor-tools-for-computer-vision) | 2015 | MATLAB | CP; Tucker; NTF | Dense compression 29 | | [MATLAB Tensor Toolbox](http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html) | [T. Toolbox classes](http://dl.acm.org/citation.cfm?doid=1186785.1186794) | 2015 | MATLAB | CP; Tucker | Dense/sparse compression 30 | | [Tensor CUR](https://github.com/arvindks/tensorcur) | [Paper](http://arxiv.org/pdf/1511.05208v3) | 2015 | Python | Tucker | Adaptive sampling 31 | | [htucker](http://anchp.epfl.ch/htucker) | [Paper](http://sma.epfl.ch/~anchpcommon/publications/htucker.pdf) | 2013 | MATLAB | HT | Dense compression; adaptive samping 32 | | [GeomCG](http://anchp.epfl.ch/geomCG) | [Paper](http://sma.epfl.ch/~anchpcommon/publications/tensorcompletion.pdf) | 2013 | MATLAB | Tucker | Tensor completion 33 | | [TDALAB](http://bsp.brain.riken.jp/TDALAB/) | [Manual](http://bsp.brain.riken.jp/~zhougx/tdalab/tdalab_guide.pdf) | 2013 | MATLAB | CP; Tucker | Dense compression 34 | | [FSTD](web.fi.uba.ar/~ccaiafa/Code/FSTD1_package.rar) | [Paper](http://ac.els-cdn.com/S0024379510001394/1-s2.0-S0024379510001394-main.pdf?_tid=2b4511cc-51c3-11e6-8551-00000aab0f01&acdnat=1469381116_ac0c8c104651f51a54c0664b7b9466cf) | 2010 | MATLAB | Tucker | Adaptive sampling 35 | | [Cross3D (variant)](http://spring.inm.ras.ru/osel/download/3d.tgz) | [Paper](http://spring.inm.ras.ru/osel/wp-content/plugins/wp-publications-archive/openfile.php?action=open&file=5) | 2008 | MATLAB | Tucker | Adaptive sampling 36 | | [N-way Toolbox](http://www.models.life.ku.dk/nwaytoolbox) | [Paper](http://www.models.life.ku.dk/sites/default/files/TheNwayToolboxforMATLAB.pdf) | 2006 | MATLAB | CP, Tucker | Dense compression (optionally with missing values) 37 | --------------------------------------------------------------------------------