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..
(Probably) Concave Graph Matching
Authors: Haggai Maron, Yaron Lipman
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments |
| Researcher Affiliation | Academia | Haggai Maron Weizmann Institute of Science Rehovot, Israel EMAIL Yaron Lipman Weizmann Institute of Science Rehovot, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1: Frank-Wolfe algorithm. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | SHREC07 (Giorgi et al., 2007) shape matching benchmark |
| Dataset Splits | No | The paper mentions using datasets like SHREC07 and Model Net10 and a 'protocol of Kim et al. (2011)', but does not explicitly provide specific train/validation/test dataset split information within its main text. |
| Hardware Specification | Yes | 150msec for n = 200 with Algorithm 1, and 16sec with Algorithm 2, both on a single CPU |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | On each shape, we sampled k = 8 points using farthest point sampling and randomized s = 2000 initializations of subsets of l = 3 points. In this stage, we use n = 300 points. We then up-sampled to n = 1500 using the exact algorithm with initialization using our n = 300 best result. |