Revisiting Decomposable Submodular Function Minimization with Incidence Relations
Authors: Pan Li, Olgica Milenkovic
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 panli2@illinois.edu Olgica Milenkovic UIUC milenkov@illinois.edu |
| 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. |