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..
Learning brain regions via large-scale online structured sparse dictionary learning
Authors: Elvis DOHMATOB, Arthur Mensch, Gael Varoquaux, Bertrand Thirion
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary xperiments on brain data show that our proposed method extracts structured and denoised dictionaries that are more intepretable and better capture inter-subject variability in small medium, and large-scale regimes alike, compared to state-of-the-art models. |
| Researcher Affiliation | Academia | Parietal Team, INRIA / CEA, Neurospin, Université Paris-Saclay, France |
| Pseudocode | Yes | Algorithm 1 Online algorithm for the dictionary-learning problem (2) and Algorithm 2 BCD dictionary update with Laplacian prior are provided. |
| Open Source Code | No | The authors implementation of the proposed Smooth-SODL (2) model will soon be made available as part of the Nilearn package [2]. |
| Open Datasets | Yes | Our experiments were done on task f MRI data from 500 subjects from the HCP Human Connectome Project dataset [20]. |
| Dataset Splits | No | The input data X were shuffled and then split into two groups of the same size. There is no explicit mention of validation splits or percentages. |
| Hardware Specification | Yes | All experiments were run on a single CPU of laptop. |
| Software Dependencies | No | The paper mentions "implemented as part of the Nilearn open-source library Python library [2]" but does not specify version numbers for Nilearn or Python. |
| Experiment Setup | Yes | Require: Regularization parameters α, γ > 0; initial dictionary V Rp k, number of passes / iterations T on the data. ... We typically use we use mini-batches of size η = 20. ... we sought a decomposition into a dictionary of k = 40 atoms (components). ... Concerning the α parameter, inspired by [26], we have found the following time-varying data-adaptive choice for the α parameter to work very well in practice: α = αt t 1/2. (10) |