Representational Similarity Learning with Application to Brain Networks
Authors: Urvashi Oswal, Christopher Cox, Matthew Lambon-Ralph, Timothy Rogers, Robert Nowak
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show, in theory and f MRI experiments, how Gr OWL deals with strongly correlated covariates. ... Before applying our framework to real f MRI data, we consider a simulation study that allows us to compare results against a known ground-truth. ... We next consider the application of group lasso and Gr OWL to the discovery of similarity structure in neural responses measured by f MRI across the whole brain while participants perform a cognitive task. |
| Researcher Affiliation | Academia | Urvashi Oswal UOSWAL@WISC.EDU Christopher Cox CRCOX@WISC.EDU Matthew A. Lambon Ralph MATT.LAMBON-RALPH@MANCHESTER.AC.UK Timothy Rogers TTROGERS@WISC.EDU Robert Nowak RDNOWAK@WISC.EDU University of Wisconsin-Madison, Madison, WI 53706, USA University of Manchester, Manchester M13 9PL, UK |
| Pseudocode | No | The paper describes mathematical formulations and algorithms conceptually but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The fMRI dataset used for the real data application was collected as part of a larger study from 23 participants at the University of Manchester. No public access link, DOI, or specific repository name is provided for this dataset. |
| Dataset Splits | Yes | For each participant, training data were divided into 9 subsets containing 4-5 stimulus events each. One subset was selected as a final hold-out set. Models were then fit at each of 10 increasing values of each λ and λ1 parameter (grid points) using 8-fold cross validation. At each fold we assessed the model using the Frobenius norm of the difference between the target Y entries and the predicted b Y = X b B entries for hold-out items (henceforth the model error). |
| Hardware Specification | No | The paper mentions that fMRI scans were collected, but it does not specify any computing hardware (e.g., CPU, GPU models, memory, cluster type) used for data analysis or model training. |
| Software Dependencies | No | The paper mentions 'standard pre-processing' and 'deconvolution procedure with a standard HRF kernel' but does not specify any software names with version numbers for dependencies. |
| Experiment Setup | Yes | We fit models by searching a grid of parameters (λ, λ1), including λ1 = 0 as the special case of Gr OWL that is group lasso. ... Each column corresponding to a voxel was normalized to be of standard deviation equal to one and a column of ones was added for bias correction. ... Models were then fit at each of 10 increasing values of each λ and λ1 parameter (grid points) using 8-fold cross validation. |