Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Understanding approximate and unrolled dictionary learning for pattern recovery
Authors: Benoît Malézieux, Thomas Moreau, Matthieu Kowalski
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we apply unrolling on pattern learning in magnetoencephalography (MEG) with the help of a stochastic algorithm and compare the performance to a state-of-the-art method. |
| Researcher Affiliation | Academia | Benoît Malézieux Université Paris-Saclay, Inria, CEA L2S, Université Paris-Saclay CNRS Centrale Supelec EMAIL Thomas Moreau Université Paris-Saclay, Inria, CEA Palaiseau, 91120, France EMAIL Matthieu Kowalski L2S, Université Paris-Saclay CNRS Centrale Supelec Gif-sur-Yvette, 91190, France EMAIL |
| Pseudocode | Yes | Algorithm 1 ISTA |
| Open Source Code | Yes | Code is available at https://github.com/bmalezieux/unrolled_dl. |
| Open Datasets | Yes | The data are generated from a random Gaussian dictionary D of size 30 50, with Bernoulli-Gaussian sparse codes z (sparsity 0.3, σ2 z = 1), and Gaussian noise (σ2 noise = 0.1) more details in Appendix A. ... We reproduce the multivariate CSC experiments of alphacsc3 (Dupr e la Tour et al., 2018) on the dataset sample of MNE (Gramfort et al., 2013) 6 minutes of recordings with 204 channels sampled at 150Hz with visual and audio stimuli. |
| Dataset Splits | No | The paper does not explicitly state specific train/validation/test splits, percentages, or sample counts for its experiments. |
| Hardware Specification | Yes | The computations have been performed on a GPU NVIDIA Tesla V100-DGXS 32GB using Py Torch (Paszke et al., 2019). |
| Software Dependencies | No | The paper mentions Py Torch and K3D-Jupyter but does not specify their version numbers. |
| Experiment Setup | Yes | The data are generated from a random Gaussian dictionary D of size 30 50, with Bernoulli-Gaussian sparse codes z (sparsity 0.3, σ2 z = 1), and Gaussian noise (σ2 noise = 0.1) more details in Appendix A. ... We optimize with projected gradient descent combined to a line search... We learn a dictionary composed of 128 atoms on 10 10 patches with FISTA and λ = 0.1 in all cases. ... with 20 unrolled iterations of FISTA and λ = 0.1. ... with 30 unrolled iterations and 100 iterations with batch size 20. ... 40 atoms of 1 second on mini batches of 10 seconds, with 30 unrolled iterations of FISTA, λscaled = 0.3, and 10 epochs with 10 iterations per epoch. |