$Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\ell_1$-$\ell_1$ Minimization: Architecture Design and Generalization Analysis$

Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\ell_1$ - $\ell_1$ Minimization: Architecture Design and Generalization Analysis

18 March 2020

Huynh Van Luong

Boris Joukovsky

Nikos Deligiannis

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Papers citing "Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\ell_1$-$\ell_1$ Minimization: Architecture Design and Generalization Analysis"

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Generalization analysis of an unfolding network for analysis-based Compressed SensingApplied and Computational Harmonic Analysis (ACHA), 2023

Vicky Kouni

Yannis Panagakis

MLT

420

09 Mar 2023

DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error BoundsIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2022

Vicky Kouni

Yannis Panagakis

541

14 May 2022

Learning to Optimize: A Primer and A BenchmarkJournal of machine learning research (JMLR), 2021

713

319

23 Mar 2021

Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical ImagingIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020

Marija Vella

João F. C. Mota

352

29 Nov 2020

Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted ℓ1\ell_1ℓ1​-ℓ1\ell_1ℓ1​ Minimization: Architecture Design and Generalization Analysis

Papers citing "Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\ell_1$-$\ell_1$ Minimization: Architecture Design and Generalization Analysis"

Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\ell_1$ - $\ell_1$ Minimization: Architecture Design and Generalization Analysis