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..
Solving Most Systems of Random Quadratic Equations
Authors: Gang Wang, Georgios Giannakis, Yousef Saad, Jie Chen
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical tests using both synthetic data and real images corroborate its improved signal recovery performance and computational efficiency relative to state-of-the-art approaches. |
| Researcher Affiliation | Academia | Key Lab of Intell. Contr. and Decision of Complex Syst., Beijing Inst. of Technology Digital Tech. Center & Dept. of Electrical and Computer Eng., Univ. of Minnesota Department of Computer Science and Engineering, Univ. of Minnesota EMAIL; EMAIL. |
| Pseudocode | Yes | Algorithm 1 Reweighted Amplitude Flow |
| Open Source Code | Yes | For reproducibility, the Matlab code of the RAF algorithm is publicly available at https://gangwg.github.io/RAF/. |
| Open Datasets | No | The paper uses 'synthetic data' where the true signal vector x was randomly generated using x N(0, I), and the i.i.d. sensing vectors ai ai N(0, I). It also mentions a 'Galaxy image' downloaded from 'http://pics-about-space.com/milky-way-galaxy.', but this is an image, not a structured public dataset with access details. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All experiments were performed using MATLAB on an Intel CPU @ 3.4 GHz (32 GB RAM) computer. |
| Software Dependencies | No | The paper mentions 'MATLAB' as the software used but does not provide a specific version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | Algorithm 1: 'maximum number of iterations T; step size µt = 2/6 and weighting parameter βi = 10/5 for real/complex Gaussian model; |S| = 3m/13 , and γ = 0.5.' Section 2.3: 'we take |S| := 3m/13 , βi β := 10, γ := 0.5, and µ := 2.' Section 4: 'Each scheme obtained the initial guess based on 200 power iterations, followed by a series of T = 2, 000 (truncated/reweighted) gradient iterations.' |