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 Highly Scalable Parallel Algorithm for Isotropic Total Variation Models
Authors: Jie Wang, Qingyang Li, Sen Yang, Wei Fan, Peter Wonka, Jieping Ye
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our approach outperforms existing state-of-the-art algorithms, especially in dealing with high-resolution, megasize images. |
| Researcher Affiliation | Collaboration | 1Arizona State University, Tempe, AZ 85287 USA 2Huawei Noahs Ark Lab, Hong Kong, China 3King Abdullah University of Science and Technology, Thuwal, Saudi Arabia |
| Pseudocode | Yes | Algorithm 1 FAD (Fast ADMM for TV Models) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their proposed FAD/pFAD method. |
| Open Datasets | No | The paper mentions using synthetic images and specific real images (e.g., moon image, MRI images) but does not provide concrete access information (link, DOI, repository, formal citation with authors/year for a public dataset) for these datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | Thus, we implement p FAD with Open MP for this experiment and test all of the algorithms on a server with four quad-core (16 processors in total) Intel Xeon 2.93GHz CPUs and 65GB memory. |
| Software Dependencies | No | The paper mentions software like Open MP, MPI, MFISTA, Split Bregman, FCSA, and Matlab, but does not provide specific version numbers for these software components or other dependencies. |
| Experiment Setup | Yes | We keep γ fixed (γ = 10) in this paper. For schemes about varying γ, we refer readers to (Boyd et al., 2011). ... We set ϵ = 10 4. ... The regularization parameter λ is set to be 0.2. ... The regularization parameter λ of p FAD is set to be 0.001. ... We use the default settings of the FCSA package. The sample ratio is about 20% and Gaussian noise N(0, 0.012) is added to the undersamples to generate Y. ... We run both FCSA-p FAD and FCSA-MFISTA for 50 iterations. |