Learning brain regions via large-scale online structured sparse dictionary learning
Authors: Elvis DOHMATOB, Arthur Mensch, Gael Varoquaux, Bertrand Thirion
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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) |