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..
Shared Space Transfer Learning for analyzing multi-site fMRI data
Authors: Tony Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew Greenshaw, Russell Greiner
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effectiveness of the proposed method for transferring between various cognitive tasks. Our comprehensive experiments validate that SSTL achieves superior performance to other state-of-the-art analysis techniques. |
| Researcher Affiliation | Academia | 1University of Alberta, Canada 2Nanjing University of Aeronautics and Astronautics, China 3Alberta Machine Intelligence Institute (Amii), Canada 4Purdue University Fort Wayne, United States |
| Pseudocode | No | The paper describes the proposed method using mathematical formulations and descriptive text, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | SSTL is an open-source technique and can also be used via our GUI-based toolbox called easy f MRI. All algorithms for generating the experimental studies are shared as parts of our GUI-based toolbox called easy f MRI4. 4https://easyfmri.learningbymachine.com/ |
| Open Datasets | Yes | Table 1 lists the 8 datasets (A to H) used for our empirical studies. These datasets are provided by Open NEURO repository... 3Available at https://openneuro.org/ and [25] Xue, G. & Aron, A.R. & Poldrack, R.A. (2008) Common neural substrates for inhibition of spoken and manual responses. Cerebral Cortex. 18(8):1923 1932. |
| Dataset Splits | Yes | In the training phase, we use a one-subject-out strategy for each training site to generate the validation set i.e., all responses of a subject are considered as the validation set, and the other responses are used as the training set. |
| Hardware Specification | Yes | Main: Giga X399, CPU: AMD Ryzen Threadripper 2920X (24 3.5 GHz), RAM: 128GB, GPU: NVIDIA Ge Force RTX 2080 SUPER (8GB memory), OS: Fedora 33, Python: 3.8.5, Pip: 20.2.3, Numpy: 1.19.2, Scipy: 1.5.2, Scikit-Learn: 0.23.2, MPI4py: 3.0.3, Py Torch: 1.6.0. |
| Software Dependencies | Yes | OS: Fedora 33, Python: 3.8.5, Pip: 20.2.3, Numpy: 1.19.2, Scipy: 1.5.2, Scikit-Learn: 0.23.2, MPI4py: 3.0.3, Py Torch: 1.6.0. |
| Experiment Setup | Yes | We tune the hyper-parameters regularization ϵ {10 2, 10 4, 10 6, 10 8}, number of features k, maximum number of iterations L by using grid search based on the performance of the validation set. As mentioned before, SSTL just sets L = 1, but other TL techniques (such as SRM, MDDL, MSMD, etc.), we consider L {1, 2, ..., 50} . For selecting the number of features k, we first let k1 = min(V, Td) for d = 1 . . . e D [4]. Then, we benchmark the performance of analysis by using k = αk1, where α = {0.1, 0.5, 1, 1.1, 1.5, 2}. We use ν-support vector machine (ν-SVM) [29] for classification analysis. |