Yassine Zniyed

Orcid: 0000-0002-6894-3449

According to our database1, Yassine Zniyed authored at least 18 papers between 2018 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
A stochastic algorithm for the ParaTuck decomposition.
Digit. Signal Process., 2025

2024
Efficient tensor decomposition-based filter pruning.
Neural Networks, 2024

Hybrid Network Compression through Tensor Decompositions and Pruning.
Proceedings of the 32nd European Signal Processing Conference, 2024

2023
Tucker Decomposition Based on a Tensor Train of Coupled and Constrained CP Cores.
IEEE Signal Process. Lett., 2023

Low-Rank Tensor Decompositions for Quaternion Multiway Arrays.
Proceedings of the IEEE International Conference on Acoustics, 2023

Tensor-Based Two-Layer Decoupling of Multivariate Polynomial Maps.
Proceedings of the 31st European Signal Processing Conference, 2023

2021
Tensor-based framework for training flexible neural networks.
CoRR, 2021

A tensor-based approach for training flexible neural networks.
Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020
Tensor train representation of MIMO channels using the JIRAFE method.
Signal Process., 2020

A TT-Based Hierarchical Framework for Decomposing High-Order Tensors.
SIAM J. Sci. Comput., 2020

Tensor methods for multisensor signal processing.
IET Signal Process., 2020

Dictionary-Based Tensor-Train Sparse Coding.
Proceedings of the 28th European Signal Processing Conference, 2020

2019
Breaking the curse of dimensionality based on tensor train : models and algorithms. (Gérer le fleau de la dimension à l'aide des trains de tenseurs : modèles et algorithmes).
PhD thesis, 2019

Multidimensional harmonic retrieval based on Vandermonde tensor train.
Signal Process., 2019

Tensor-Train Modeling for Mimo-OFDM Tensor Coding-and-Forwarding Relay Systems.
Proceedings of the 27th European Signal Processing Conference, 2019

Uniqueness of Tensor Train Decomposition with Linear Dependencies.
Proceedings of the 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019

Breaking the Curse of Dimensionality for Coupled Matrix-Tensor Factorization.
Proceedings of the 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019

2018
High-Order CPD Estimation with Dimensionality Reduction Using a Tensor Train Model.
Proceedings of the 26th European Signal Processing Conference, 2018


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