• FAuST
    • Learning, Sparsity, Fast transform, Multilayer sparse factorisation
    • FAuST allows to approximate a given dense matrix by a product of sparse matrices, with considerable potential gains in terms of storage and speedup for matrix-vector multiplications.
    • FAUST is a C++ toolbox designed to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation). Faust includes Matlab and Python wrappers and scripts to reproduce the experimental results of the following papers: - Le Magoarou L. and Gribonval R,. "Flexible multi-layer sparse approximations of matrices and applications", Journal of Selected Topics in Signal Processing, 2016. - Le Magoarou L., Gribonval R., Tremblay N. "Approximate fast graph Fourier transforms via multi-layer sparse", IEEE Transactions on Signal and Information Processing over Networks, 2018 - Quoc-Tung Le, Rémi Gribonval. Structured Support Exploration For Multilayer Sparse Matrix Factorization. ICASSP 2021 – IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2021, Toronto, Ontario, Canada. pp.1-5. - Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, et al.. Fast Multiscale Diffusion on Graphs. 2021.
    • In 2022, major efforts were put to optimize code efficiency (in particular for so-called butterly structures), and an anaconda package was made available. New Faust implementations of toeplitz, circulant, dct and dst matrices and more were made available. In 2021, new algorithms bringing improved precision and/or accelerations were incorporated into Faust, GPU support was completed together with a systematic optimization of the code (including the ability to run it in float instead of double precision), and PIP packages were made available to ease the installation of faust. In 2020, major efforts were put into finalizing Python wrappers, producing tutorials using Jupyter notebooks and Matlab livescripts, as well as substantial refactoring of the code to optimize its efficiency and exploit GPUs. In april 2018, a Software Development Initiative (ADT REVELATION) started in for the maturation of FAuST. A first step was to complete and robustify Matlab wrappers, to code Python wrappers with the same functionality, and to setup a continuous integration process. A second step was to simplify the parameterization of the main algorithms. The roadmap for next year includes showcasing examples and optimizing computational efficiency. In 2017, new Matlab code for fast approximate Fourier Graph Transforms have been included. based on the approach described in the papers: -Luc Le Magoarou, Rémi Gribonval, "Are There Approximate Fast Fourier Transforms On Graphs?", ICASSP 2016 . -Luc Le Magoarou, Rémi Gribonval, Nicolas Tremblay, "Approximate fast graph Fourier transforms via multi-layer sparse approximations", IEEE Transactions on Signal and Information Processing over Networks,2017.
    • Faust 1.x contains Matlab routines to reproduce experiments of the PANAMA team on learned fast transforms. Faust 2.x contains a C++ implementation with preliminary Matlab / Python wrappers. Faust 3.x includes Python and Matlab wrappers around a C++ core with GPU acceleration, new algorithms.
      • Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, et al.. Fast Multiscale Diffusion on Graphs. ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapore, Singapore. ⟨10.1109/ICASSP43922.2022.9746802⟩. ⟨hal-03212764v2⟩
      • Luc Le Magoarou, Rémi Gribonval, Nicolas Tremblay. Approximate fast graph Fourier transforms via multi-layer sparse approximations. IEEE Transactions on Signal and Information Processing over Networks, 2018, 4 (2), pp.407--420. ⟨10.1109/TSIPN.2017.2710619⟩. ⟨hal-01416110v3⟩
      • Luc Le Magoarou, Nicolas Tremblay, Rémi Gribonval. Analyzing the Approximation Error of the Fast Graph Fourier Transform. ACSSC 2017 - 51st Annual Asilomar Conference on Signals Systems and Computers, Oct 2017, Monterey, California, United States. ⟨hal-01627434⟩
      • Luc Le Magoarou, Rémi Gribonval. Flexible Multi-layer Sparse Approximations of Matrices and Applications. IEEE Journal of Selected Topics in Signal Processing, 2016, 10 (4), pp.688-700. ⟨10.1109/JSTSP.2016.2543461⟩. ⟨hal-01167948v2⟩
      • Luc Le Magoarou, Rémi Gribonval. Are There Approximate Fast Fourier Transforms On Graphs? . International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2016, Shanghai, China. ⟨hal-01254108v2⟩
      • Luc Le Magoarou. Matrices efficientes pour le traitement du signal et l'apprentissage automatique. Traitement du signal et de l'image [eess.SP]. INSA de Rennes, 2016. Français. ⟨NNT : 2016ISAR0008⟩. ⟨tel-01412558⟩
      • Luc Le Magoarou, Rémi Gribonval, Alexandre Gramfort. FA$\mu$ST: speeding up linear transforms for tractable inverse problems. European Signal Processing Conference (EUSIPCO), Aug 2015, Nice, France. ⟨hal-01156478⟩
      • Luc Le Magoarou, Rémi Gribonval. Chasing butterflies: In search of efficient dictionaries. International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2015, Brisbane, Australia. ⟨10.1109/ICASSP.2015.7178579⟩. ⟨hal-01104696v2⟩
      • Luc Le Magoarou, Rémi Gribonval. Multi-layer Sparse Matrix Factorization. SPARS 2015 Signal Processing with Adaptive Sparse Structured Representations, Jul 2015, Cambridge, United Kingdom. ⟨hal-01158057⟩
      • Quoc-Tung Le, Rémi Gribonval. Structured Support Exploration For Multilayer Sparse Matrix Factorization. ICASSP 2021 - IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2021, Toronto, Ontario, Canada. pp.1-5, ⟨10.1109/ICASSP39728.2021.9414238⟩. ⟨hal-03132013⟩
    • Luc Le Magoarou, Nicolas Tremblay, Remi Gribonval (remi.gribonval@inria.fr), Nicolas Bellot, Adrien Leman, Hakim Hadj-djilani (hakim.hadj-djilani@inria.fr)
    • Remi Gribonval (remi.gribonval@inria.fr)
    • https://faust.inria.fr/