Sparse Bayesian structure learning with “dependent relevance determination” priors
Authors: Anqi Wu, Mijung Park, Oluwasanmi O Koyejo, Jonathan W Pillow
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Sec. 6, we show applications to simulated data and neuroimaging data. Beginning with simulated data, we compare performances of various regression estimators. |
| Researcher Affiliation | Academia | 1,4 Princeton Neuroscience Institute, Princeton University, {anqiw, pillow}@princeton.edu 2 The Gatsby Unit, University College London, mijung@gatsby.ucl.ac.uk 3 Department of Psychology, Stanford University, sanmi@stanford.edu |
| Pseudocode | No | The paper describes computational procedures but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code is made publicly available. |
| Open Datasets | Yes | We analyzed functional MRI data from the Human Connectome Project 1 collected from 215 healthy adult participants on a relational reasoning task. ... 1http://www.humanconnectomeproject.org/. |
| Dataset Splits | Yes | In each run, we randomly split the f MRI data into five sets for five-fold cross-validation, and took an average of test errors across five folds for each run. Hyperparameters were chosen by a five-fold cross-validation within the training set, and the optimal hyper parameter set was used for computing test performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions the 'flirt apply Xfm tool' but does not provide specific version numbers for software dependencies needed to replicate the experiment. |
| Experiment Setup | No | The paper states that hyperparameters were chosen via cross-validation but does not provide the specific values for these hyperparameters or other concrete experimental setup details like learning rates, batch sizes, or optimizer settings. |