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..
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices
Authors: Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is demonstrated extensively on compressive sensing of imagery and video, and the results with simulated and hardware-acquired real measurements show significant performance improvement over state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Duke University 2Department of Statistics & Department of Computer Science, University of Chicago |
| Pseudocode | No | The paper describes the steps of its variational EM algorithm but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper mentions a website for complete results and other settings, but it does not explicitly state that the source code for the methodology is available there or elsewhere. |
| Open Datasets | Yes | The USPS handwritten digits dataset 3 and the face dataset [28] are used in this experiment. It is downloaded from http://cs.nyu.edu/ roweis/data.html. |
| Dataset Splits | No | The paper describes the datasets used and the number of images/frames, but it does not provide specific training, validation, or test split percentages, counts, or explicit instructions for reproducibility. |
| Hardware Specification | No | The paper mentions 'hardware-acquired real measurements' and 'CACTI camera [6]' which refer to the data acquisition hardware, not the computing hardware (CPUs, GPUs, etc.) used for running the experiments. |
| Software Dependencies | No | The paper mentions various methods/algorithms (e.g., KSVD-OMP, Tw IST, GAP) and a 'single pixel camera' setup, but it does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For the proposed methods, the hyperparameters of the scaled mixture of Gaussians are set as pa0/b0/N ≈ 300, c0 = d0 = 10^-6, the hyperparameter of Dirichlet prior α0 is set as a vector with all elements being one, the hyperparameters of the mean of each Gaussian component are set as β0 = 1, and m0 is set to the mean of the initialization of {bxi}N i=1. We fixed κ = 10^-6 for the proposed methods, GMM-TP and PLE. |