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.
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 | Input Tokens: 15,642 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,530 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 | 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. |