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..
Block Sparse Bayesian Learning: A Diversified Scheme
Authors: Yanhao Zhang, Zhihan Zhu, Yong Xia
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments validate the advantages of Div SBL over existing algorithms. In this section, we compare Div SBL with the following six algorithms:4 1. Block-based algorithms: (1) BSBL, (2) Group Lasso, (3) Group BPDN. 2. Pattern-based algorithms: (4) PC-SBL, (5) Struct OMP. 3. Sparse learning (without structural information): (6) SBL. Results are averaged over 100 or 500 random runs (based on computational scale), with SNR ranging from 15-25 d B except the test for varied noise levels. Normalized Mean Squared Error (NMSE) , defined as ||ˆx xtrue||2 2/||xtrue||2 2, and Correlation (Corr) (cosine similarity) are used to compare algorithms. |
| Researcher Affiliation | Academia | Yanhao Zhang Zhihan Zhu Yong Xia School of Mathematical Sciences, Beihang University Beijing, 100191 EMAIL |
| Pseudocode | Yes | In conclusion, the Diversified SBL (Div SBL) algorithm is summarized as Algorithm 1 below. The procedure, using dual ascent method to diversify Bi, is summarized in Algorithm 2 as follows: |
| Open Source Code | Yes | Matlab codes for our algorithm are available at https://github.com/Yanhao Zhang1/Div SBL . |
| Open Datasets | Yes | We initially test on synthetic signal data, including homoscedastic (provided by [24]) and heteroscedastic data... randomly chosen in Audio Set [34]... In 2D image experiments, we utilize a standard set of grayscale images compiled from two sources 6. Available at http://dsp.rice.edu/software/DAMP-toolbox and http://see.xidian.edu.cn/faculty/wsdong/NLR_Exps.htm |
| Dataset Splits | No | The paper does not explicitly provide details about a validation dataset split (e.g., percentages, sample counts, or methodology for a dedicated validation set). |
| Hardware Specification | No | The paper mentions 'CPU time' in Appendix D but does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | Matlab codes for our algorithm are available at https://github.com/Yanhao Zhang1/Div SBL . While implying the use of Matlab, the paper does not specify version numbers for Matlab or any other key software components used in the experiments. |
| Experiment Setup | Yes | Results are averaged over 100 or 500 random runs (based on computational scale), with SNR ranging from 15-25 d B except the test for varied noise levels. The sensitivity to initialization on the heteroscedastic signal from Section 5.1. Initial variances are set to γ = η ones(g L, 1) and γ = η rand(g L, 1) with the scale parameter η ranging from 1 10 1 to 1 104. Input: Measurement matrix Φ, response y, initialized variance γ, prior s covariance Σ0, noise s variance β, and multipliers λ0. |