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..
Latent Sparse Modeling of Longitudinal Multi-Dimensional Data
Authors: Ko-Shin Chen, Tingyang Xu, Jinbo Bi
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational results on synthetic datasets and realfile f MRI and EEG problems demonstrate the superior performance of the proposed approach over existing techniques. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA EMAIL, EMAIL 2 Tencent AI Lab, Shenzhen, China, EMAIL |
| Pseudocode | Yes | Algorithm 1 Search for optimal ˆΦ |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code for their methodology is released. |
| Open Datasets | Yes | The f MRI data used in the experiment were collected by the Alzheimer s Disease Neuroimaging Initiative (ADNI)1. 1http://adni.loni.usc.edu/ |
| Dataset Splits | Yes | We randomly select 80% of the subjects for training and the rest for testing...The λ1, λ2, and λ3 were tuned in a two-fold cross validation. In other words, the training records were further split into half: one used to build a model with a chosen parameter value from a range of 1 to 20 with a step size of 0.1; and the other used to test the resultant model. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In our experiments, λ s are tuned as λ1 = λ2 = λ3 = 0.3 based on cross validation within training...The λ1, λ2, and λ3 were tuned in a two-fold cross validation. In other words, the training records were further split into half: one used to build a model with a chosen parameter value from a range of 1 to 20 with a step size of 0.1; and the other used to test the resultant model...The hyperparameters λ1, λ2, and λ3 in our approach and GEE/PGEE (one parameter) were tuned in a two-fold cross validation within the training data. |