Learning step sizes for unfolded sparse coding
Authors: Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Experiments This section provides numerical arguments to compare SLISTA to LISTA and ISTA. All the experiments were run using Python [Python Software Foundation, 2017] and pytorch [Paszke et al., 2017]. The code to reproduce the figures is available online2. Network comparisons We compare the proposed approach SLISTA to state-of-the-art learned methods LISTA [Chen et al., 2018] and ALISTA [Liu et al., 2019] on synthetic and semi-real cases. ... Figure 6 shows the test curves for different levels of regularization λ = 0.1 and 0.8. |
| Researcher Affiliation | Academia | Pierre Ablin , Thomas Moreau , Mathurin Massias, Alexandre Gramfort Inria CEA Université Paris-Saclay {pierre.ablin,thomas.moreau,mathurin.massias,alexandre.gramfort}@inria.fr |
| Pseudocode | Yes | Algorithm 1: Oracle-ISTA (OISTA) with larger step sizes |
| Open Source Code | Yes | The code to reproduce the figures is available online2. 2 The code can be found at https://github.com/tom Moral/adopty |
| Open Datasets | Yes | For the semi-real case, we used the digits dataset from scikit-learn [Pedregosa et al., 2011] |
| Dataset Splits | No | The networks are trained by minimizing the empirical loss L (15) on a training set of size Ntrain = 10, 000 and we report the loss on a test set of size Ntest = 10, 000 . No explicit mention of a validation set or its size is made. |
| Hardware Specification | No | The paper does not specify any hardware used for the experiments, such as CPU or GPU models. |
| Software Dependencies | No | All the experiments were run using Python [Python Software Foundation, 2017] and pytorch [Paszke et al., 2017]. The paper mentions software names but does not provide specific version numbers for them, which is required for reproducibility. |
| Experiment Setup | No | The paper states: 'Further details on training are in Appendix D.' This indicates that specific experimental setup details, such as hyperparameters or optimizer settings, are not provided in the main text. |