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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Decomposable Submodular Function Minimization: Discrete and Continuous
Authors: Alina Ene, Huy Nguyen, László A. Végh
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,787 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,277 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our paper establishes connections between discrete and continuous methods for DSFM, as well as provides a systematic experimental comparison of these approaches. |
| Researcher Affiliation | Academia | Department of Computer Science, Boston University, EMAIL College of Computer and Information Science, Northeastern University, EMAIL Department of Mathematics, London School of Economics, EMAIL |
| Pseudocode | No | The paper describes algorithms (e.g., alternating projections, random coordinate descent) but does not provide pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit links to source code for the methodology described, nor does it state that the code is publicly available. |
| Open Datasets | Yes | The data is available at http://melodi.ee.washington.edu/~jegelka/cc/index.html and http://research.microsoft.com/en-us/um/cambridge/projects/visionimagevideoediting/ segmentation/grabcut.htm |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). It mentions using "five image segmentation instances" for evaluation. |
| Hardware Specification | Yes | The experiments were carried out on a single computer with a 3.3 GHz Intel Core i5 processor and 8 GB of memory |
| Software Dependencies | No | The paper mentions several algorithms and tools (RCDM, ACDM, Submodular IBFS, Fujishige-Wolfe, general QP solver), but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | At every iteration, we ran Fujishige-Wolfe for 10 iterations only, but we warm-started with the current solution xi B(fi) for each i [r]. |