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..
A Provable Approach for Double-Sparse Coding
Authors: Thanh Nguyen, Raymond Wong, Chinmay Hegde
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we support our analysis via several numerical experiments on simulated data, confirming that our method can indeed be useful in problem sizes encountered in practical applications. |
| Researcher Affiliation | Academia | Thanh V. Nguyen ECE Department Iowa State University EMAIL Raymond K. W. Wong Statistics Department Texas A&M University EMAIL Chinmay Hegde ECE Department Iowa State University EMAIL |
| Pseudocode | Yes | Algorithm 1 Truncated Pairwise Reweighting; Algorithm 2 Double-Sparse Coding Descent Algorithm |
| Open Source Code | Yes | Matlab implementation of our algorithms is available online4. 4https://github.com/thanh-isu/double-sparse-coding |
| Open Datasets | No | We generate a synthetic training dataset according to the model described in the Setup. The base dictionary Φ is the identity matrix of size n = 64 and the square synthesis matrix A is a block diagonal matrix with 32 blocks. Each 2x2 block is of form [1 1; 1 1] (i.e., the column sparsity r = 2) . The support of x is drawn uniformly over all 6-dimensional subsets of [m], and the nonzero coefficients are randomly set to 1 with equal probability. |
| Dataset Splits | No | The paper mentions 'disjoint sets P1 and P2 of sizes p1 and p2 respectively' for the initialization stage, but it does not specify explicit training/validation/test splits for the overall experimental evaluation of the model's performance. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab implementation of our algorithms' and refers to 'the implementation of Trainlets' but does not provide specific version numbers for Matlab or any other software libraries/dependencies. |
| Experiment Setup | Yes | For all the approaches except Trainlets, we use T = 2000 iterations for the initialization procedure, and set the number of steps in the descent stage to 25. ... The learning step of Trainlets is executed for 50 iterations. |