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..
Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model
Authors: Jie Xu, Cheng Deng, Xinbo Gao, Dinggang Shen, Heng Huang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on cognitive data of the ADNI cohort demonstrate the superior performance of the proposed method. |
| Researcher Affiliation | Academia | Jie Xu1,3, Cheng Deng1, Xinbo Gao1, Dinggang Shen2, Heng Huang3,1 1Xidian University, Xi an 710071, China 2Department of Radiology and BRIC, UNC-Chapel Hill, USA 3University of Texas at Arlington, USA |
| Pseudocode | Yes | Algorithm 1 The algorithm to solve Eq. (6) |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | we evaluate prediction performance of the proposed method by applying it to Alzheimer s Disease Neuroimaging Initiative (ADNI) cohort (adni.loni.usc.edu) |
| Dataset Splits | Yes | In all experiments, we automatically tune the regularization parameters by selecting among the values {10r : r { 5, ..., 5}} with standard 5-fold cross-validation strategy. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we automatically tune the regularization parameters by selecting among the values {10r : r { 5, ..., 5}} with standard 5-fold cross-validation strategy. |