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..
Deep Hyperalignment
Authors: Muhammad Yousefnezhad, Daoqiang Zhang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies on multi-subject f MRI analysis confirm that the DHA method achieves superior performance to other state-of-the-art HA algorithms. |
| Researcher Affiliation | Academia | Muhammad Yousefnezhad, Daoqiang Zhang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics EMAIL |
| Pseudocode | Yes | Algorithm 1 Deep Hyperalignment (DHA) |
| Open Source Code | No | The paper states: 'This paper provides a detailed description of HA methods in the supplementary materials (https://sourceforge.net/ projects/myousefnezhad/files/DHA/)'. This statement describes the content of the link as a 'detailed description' rather than explicitly stating it contains the source code for the methodology presented in this paper. |
| Open Datasets | Yes | This paper utilizes 5 datasets, shared by Open f MRI (https://openfmri.org), for running empirical studies of this section. |
| Dataset Splits | Yes | In addition, leave-one-subject-out cross-validation is utilized for partitioning datasets to the training set and testing set. |
| Hardware Specification | Yes | 2DEL, CPU = Intel Xeon E5-2630 v3 (8 2.4 GHz), RAM = 64GB, GPU = Ge Force GTX TITAN X (12GB memory), OS = Ubuntu 16.04.3 LTS, Python = 3.6.2, Pip = 9.0.1, Numpy = 1.13.1, Scipy = 0.19.1, Scikit-Learn = 0.18.2, Theano = 0.9.0. |
| Software Dependencies | Yes | Python = 3.6.2, Pip = 9.0.1, Numpy = 1.13.1, Scipy = 0.19.1, Scikit-Learn = 0.18.2, Theano = 0.9.0. |
| Experiment Setup | Yes | Consequently, three hidden layers (C = 5) and the regularized parameters ϵ = {10 4, 10 6, 10 8} are employed in the DHA method. In addition, the number of units in the intermediate layers are considered U (m) = KV , where m = 2:C-1, C is the number of layers, V denotes the number of voxels and K is the number of stimulus categories in each dataset1. Further, three distinctive activation functions are employed, i.e. Sigmoid (g(x) = 1/1 + exp( x)), Hyperbolic (g(x) = tanh(x)), and Rectified Linear Unit or Re LU (g(x) = ln(1 + exp(x)). |