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..
Revisiting Decomposable Submodular Function Minimization with Incidence Relations
Authors: Pan Li, Olgica Milenkovic
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In what follows, we illustrate the performance of the newly proposed DSFM algorithms on a benchmark datasets used for MAP inference in image segmentation [9] and used for semi-supervised learning over graphs |
| Researcher Affiliation | Academia | Pan Li UIUC EMAIL Olgica Milenkovic UIUC EMAIL |
| Pseudocode | Yes | Algorithm 1: Parallel RCDM for Solving (4) |
| Open Source Code | Yes | The code for this work can be found in https://github.com/lipan00123/DSFM-with-incidence-relations. |
| Open Datasets | Yes | We used two images oct and smallplant adopted from [14]2. ... 2Downloaded from the website of Professor Stefanie Jegelka: http://people.csail.mit.edu/stefje/code.html |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, specific percentages, or sample counts needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU/CPU models, memory details, or cloud resources. |
| Software Dependencies | No | The paper does not provide specific version numbers for software components like programming languages, libraries, or solvers. |
| Experiment Setup | Yes | In our case, as we are only concerned with the convergence rate of the algorithm, we fix τ = 0.1. |