Publications

Publications are available in HAL

 Submitted:
  • Y. Zniyed, K. Usevich, S. Miron and D. Brie, ''Tensor-based framework for training flexible neural networks'', submitted.

Journal papers: [Hal archives]

  1. Y. Zniyed and A.L.F. de Almeida, ''A stochastic algorithm for the ParaTuck decomposition'', Digital Signal Processing, 2024, accepted.
  2. V.T. Pham, Y. Zniyed and T.P. Nguyen, ''Efficient tensor decomposition-based filter pruning'', Neural Networks, 2024, accepted. [CODE]
  3. V.T. Pham, Y. Zniyed and T.P. Nguyen, ''Enhanced network compression through tensor decompositions and pruning'', IEEE Transactions on Neural Networks and Learning Systems, 2024, accepted. [CODE]
  4. M. Giraud, V. Itier, R. Boyer, Y. Zniyed and A.L.F. de Almeida, ''Tucker decomposition based on a tensor train of coupled and constrained CP cores'', IEEE Signal Processing Letters, vol. 30, pp. 758-762, 2023. 
  5. S. Miron, Y. Zniyed, R. Boyer, A.L.F. de Almedia, G. Favier, D. Brie and P. Comon, "Tensor methods for multisensor signal processing",  IET Signal Processing, 14, (10), p. 693-709, 2021.
  6. Y. Zniyed, R. Boyer, A.L.F. de Almedia and G. Favier, "A TT-based hierarchical framework for decomposing high-order tensors", SIAM Journal on Scientific Computing (SISC), vol. 42, pp. 822-848, 2020.
  7. Y. Zniyed, R. Boyer, A.L.F. de Almedia and G. Favier, "Tensor Train Representation of Massive MIMO Channels using the Joint Dimensionality Reduction and Factor Retrieval (JIRAFE) Method", Elsevier, Signal Processing (SP), vol. 171, Article 107479, 2020.
  8. Y. Zniyed, R. Boyer, A.L.F. de Almedia and G. Favier, "High-order tensor estimation via trains of coupled third-order CP and Tucker decompositions", Linear Algebra and its Applications (LAA), vol. 588, pp. 304-337, 2020.
  9. Y. Zniyed, R. Boyer, A.L.F. de Almedia and G. Favier, "Multidimensional Harmonic Retrieval Based on Vandermonde Tensor Train", Elsevier,  Signal Processing (SP), vol. 163, pp. 75-86, 2019.
Conference papers: [Hal archives]
  1. V.T. Pham, Y. Zniyed and T.P. Nguyen, "Hybrid network compression through tensor decompositions and pruning", in Proc. of the 32nd European Signal Processing Conference, EUSIPCO'24, Lyon, France, 2024.
  2. K. Usevich, Y. Zniyed, M. Ishteva, P. Dreesen and A.L.F. de Almeida, "Tensor-based two-layer decoupling of multivariate polynomial maps", in Proc. of the 31st European Signal Processing Conference, EUSIPCO'23, Helsinki, Finland, 2023.
  3. V.T. Pham, Y. Zniyed and T.P. Nguyen, "Élagage efficace des filtres basé sur les décompositions tensorielles", GRETSI 2023, Grenoble, France
  4. M. Giraud, V. Itier, R. Boyer, Y. Zniyed and A.L.F. de Almeida, "Décomposition de Tucker basée sur un train de tenseurs avec des cœurs CP contraints couplés", GRETSI 2023, Grenoble, France.
  5. O. Imhogiemhe, J. Flamant, X. Luciani, Y. Zniyed and S. Miron, "Low-rank tensor decompositions for quaternion multiway arrays", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Greek island of Rhodes, 2023.
  6. J. Flamant, X. Luciani, Y. Zniyed and S. Miron, "Tenseurs à valeurs quaternioniques : un objet mathématique à identifier", GRETSI 2022, Nancy, France.
  7. Y. Zniyed, K. Usevich, S. Miron and D. Brie, "Une approche tensorielle pour entrainer des réseaux de neurones flexibles", GRETSI 2022, Nancy, France.
  8. Y. Zniyed, K. Usevich, S. Miron and D. Brie, "Tensor-based approach for training flexible neural networks", ASILOMAR, Pacific Grove, California, US, Nov 2021.
  9. Y. Zniyed, K. Usevich, S. Miron and D. Brie, "Learning nonlinearities in the decoupling problem with structured CPD", IFAC symposium on System Identification, Padova, Italy, 2021.
  10. A. Boudehane, Y. Zniyed, A. Tenenhaus, L. Le Brusquet and R. Boyer, "Dictionary-based tensor-train sparse coding", in Proc. of the 28th European Signal Processing Conference, EUSIPCO'20, Netherlands, 2021.
  11. Y. Zniyed, S. Miron, R. Boyer and D. Brie, "Uniqueness of Tensor Train Decomposition with Linear Dependencies", in Proc. of IEEE CAMSAP, 2019, Le Gosier, Guadeloupe.
  12. A. Boudehane, Y. Zniyed, A. Tenenhaus, L. Le Brusquet and R. Boyer, "Breaking The Curse of Dimensionality for Coupled Matrix-Tensor Factorization", in Proc. of IEEE CAMSAP, 2019, Le Gosier, Guadeloupe.
  13. Y. Zniyed, S. Miron, R. Boyer and D. Brie, "Uniqueness of Tensor Train Decomposition with Linear Dependencies", GRETSI, 2019, Lille, France.
  14. Y. Zniyed, R. Boyer, A.L.F. de Almedia and G. Favier, "Tensor-Train Modeling for MIMO-OFDM Tensor Coding-And-Forwarding Relay Systems", in Proc. of the 27th European Signal Processing Conference, EUSIPCO'19, Spain.
  15. Y. Zniyed, R. Boyer, A.L.F. de Almedia and G. Favier, "High-Order CPD Estimation with Dimensionality Reduction Using A Tensor Train Model", in Proc. of the 26th European Signal Processing Conference, EUSIPCO'18, Rome, Italy.

Book chapters: [Hal archives]

  1. Y. Zniyed, O. Karmouda, R. Boyer, J. Boulanger, A.L.F. de Almedia and G. Favier, ''Structured Tensor-Train Decomposition for Speeding-Up Kernel-Based Learning'', Tensors for Data Processing, Edited by Yipeng Liu, chapter 14, pp. 537-563, 2022.