Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Solving Most Systems of Random Quadratic Equations
Authors: Gang Wang, Georgios Giannakis, Yousef Saad, Jie Chen
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,541 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,186 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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.' |