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..
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
Authors: Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Simulations: The paper [8] presented several numerical experiments to assess the performance of EM-VAMP relative to other methods. Here, our goal is to conο¬rm that EM-VAMP s performance matches the SE predictions. |
| Researcher Affiliation | Academia | Alyson K. Fletcher Dept. Statistics UC Los Angeles EMAIL; Mojtaba Sahraee-Ardakan Dept. EE, UC Los Angeles EMAIL; Sundeep Rangan Dept. ECE, NYU EMAIL; Philip Schniter Dept. ECE, The Ohio State Univ. EMAIL |
| Pseudocode | Yes | Algorithm 1 Adaptive VAMP |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The numerical experiments use synthetic data and an image described as 'An N = 256 256 image of a satellite'. The paper does not provide concrete access information (link, DOI, repository, or proper citation with author/year for public dataset) for a publicly available dataset used for training. |
| Dataset Splits | No | The paper mentions evaluating performance but does not specify exact training, validation, and test dataset splits or cross-validation methods. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes parameters of the simulated data (e.g., matrix dimensions, condition number, sparsity level) but does not provide specific algorithmic hyperparameters or system-level training settings like learning rates, batch sizes, or optimizer configurations. |