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..
Global Identifiability of $\ell_1$-based Dictionary Learning via Matrix Volume Optimization
Authors: Jingzhou Hu, Kejun Huang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we propose algorithms to solve the new proposed formulation, specifically one based on the linearized-ADMM with efficient per-iteration updates. The proposed algorithms exhibit surprisingly effective performance in correctly and efficiently recovering the dictionary, as demonstrated in the numerical experiments. |
| Researcher Affiliation | Academia | Jingzhou Hu Kejun Huang Department of Computer and Information Science and Engineering University of Florida Gainesville, FL 32611 (jingzhouhu,kejun.huang)@ufl.edu |
| Pseudocode | Yes | Algorithm 1 Solving (7) with L-ADMM |
| Open Source Code | No | The paper does not provide explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | For π= 20 and π= 1000, we randomly generate the groundtruth sparse coefficient matrix πΊ according to the Bernoulli-Gaussian model with π= 0.5, and the groundtruth dictionary matrix π¨ completely random, and generate the data matrix πΏ= π¨ πΊ . For a given image, it is first divided into 8 8 non-overlapping patches, reshaped into a vector in Rπwith π= 64, and stacked as columns of the data matrix πΏ. |
| Dataset Splits | No | The paper generates synthetic data and uses image patches from a natural image, but does not describe any train/validation/test dataset splits. |
| Hardware Specification | No | The paper states that experiments are conducted in MATLAB but does not provide specific details on the hardware used (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using MATLAB for experiments but does not provide specific version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | For π= 20 and π= 1000, we randomly generate the groundtruth sparse coefficient matrix πΊ according to the Bernoulli-Gaussian model with π= 0.5, and the groundtruth dictionary matrix π¨ completely random, and generate the data matrix πΏ= π¨ πΊ . We empirically found that setting π= ππworks very well in practice. |