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..
Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks
Authors: Yizhe Zhang, Ricardo Henao, Chunyuan Li, Lawrence Carin
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Applications to image denoising, inpainting and depth-information restoration demonstrate that the proposed model outperforms other leading Bayesian dictionary learning approaches. We present experiments on two sets of images. The results on gray-scale images for denoising and inpainting tasks highlight how characterization of spatial structure improves results. |
| Researcher Affiliation | Academia | Duke University, Durham NC EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an unambiguous statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We applied our methods to the 30 images of the Middlebury stereo dataset (Scharstein and Szeliski, 2002; Lu et al., 2014). |
| Dataset Splits | No | The paper describes MCMC iterations and sample collection for image reconstruction, but does not provide explicit train/validation/test dataset splits with percentages, sample counts, or predefined citations for data partitioning. |
| Hardware Specification | Yes | All the experiments were conducted on a single machine with two 2.7 GHz processors and 12 GB RAM. For each MCMC iteration, computations were parallelized w.r.t. dictionary elements using a desktop GPU. |
| Software Dependencies | No | The paper states 'code written in Matlab and C++', but does not provide specific version numbers for these or any other key software components or libraries. |
| Experiment Setup | Yes | The hyper-parameters controlling Gaussian distribution variances, i.e., σb and σλ, were all set to 0.1. As suggested in Zhou et al. (2009), the hyper-parameters for the inverse Gamma distributions (the priors for σw and σ") were set to {10^-6, 10^-6}. Dictionary sizes in both GPFA and GP-SBN-FA are initially set to 128. In GP-SBN-FA, we use a one-layer SBN with the number of top-layer binary units L set to half the size of the dictionary M. |