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 [1].
A convex program for bilinear inversion of sparse vectors
Authors: Alireza Aghasi, Ali Ahmed, Paul Hand, Babhru Joshi
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide numerical experiments on synthetic and real data where the signals follow the multiplicative model (1), which is compatible with physics of lighting (Hold [1986]). |
| Researcher Affiliation | Academia | Alireza Aghasi Georgia State Business School GSU, GA EMAIL Ali Ahmed Dept. of Electrical Engineering ITU, Lahore EMAIL Paul Hand Dept. of Mathematics and College of Computer and Information Science Northeastern University, MA EMAIL Babhru Joshi Dept. of Computational and Applied Mathematics Rice University, TX babhru. EMAIL |
| Pseudocode | No | The paper describes ADMM steps and equations but does not present them in a structured pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses "synthetic and real data" but does not provide specific access information (links, DOIs, formal citations) for publicly available datasets. The real images (mousepad, rice grains) are instances used in their experiments without public access details. |
| Dataset Splits | No | The paper conducts numerical experiments and discusses trials but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper describes an ADMM implementation but does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions). |
| Experiment Setup | Yes | We solve (3) using an ADMM implementation similar to the ADMM implementation detailed in Section 2 with the step size parameter = 1. ... Let (ˆh, ˆm, ˆ ) be the output of (5) with λ = 103 and = 10 4. ... Let (ˆh, ˆm, ˆ ) be the output of (5) with λ = 103 and = 10 7. |