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..
Solving Partial Assignment Problems using Random Clique Complexes
Authors: Charu Sharma, Deepak Nathani, Manohar Kaul
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present a comprehensive empirical study that compares our method s matching accuracy to that of a diverse set of matching approaches (Zhou & De la Torre, 2016; Zhou & De la Torre, 2013; Cho et al., 2010; Feizi et al., 2016; Leordeanu & Hebert, 2005; Cour et al., 2007; Pachauri et al., 2013; Gold & Rangarajan, 1996; Kuhn, 1955; Leordeanu et al., 2009; Zass & Shashua, 2008; Li et al., 2013; Duchenne et al., 2011). We conducted our experiments on both synthetic and well-known hard real-world datasets that span across affine/non-affine transformations, severe occlusions, and clutter. Our study reveals much better accuracy for the popular datasets against several of the state-of-the-art matching methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India. |
| Pseudocode | Yes | Algorithm 1 Matching Random Clique Complexes Input: X(G) = {G(k,l)}h k=0 and X(G ) = {G (k,l)}h k=0 1: for k = h . . . 0 do 2: Let M, M be the total number of (k + 1)-cliques in G(k,l) and G (k,l), respectively 3: L := {c(k) i }M 1 i=0 # list of barycenters 4: for i = 0 . . . M 1 do 5: Ni := Ni n g(k,l) (x,:) | x = i, g(k,l) (x,y) = 0 o 6: N := N {Ni} # clique neighborhoods 7: end for 8: for i = 0 . . . M 1 do 9: αi := [α1, . . . , αM 1]T 10: α := α {αi} # affine weight vectors 11: end for 12: Repeat steps 3 11 on G (k,l) for L , N and α . 13: Build cost matrix C(k) from weights vectors α, α 14: X k := Kuhn-Munkres (G(k,l), G (k,l), C(k)) 15: end for Return: {X 0, . . . , X h} # set of permutation matrices |
| Open Source Code | No | No explicit statement or link indicating that the source code for *this paper's* methodology is publicly available. |
| Open Datasets | Yes | We took two real-world datasets, i.e., Books and Building (Pachauri et al., 2013) |
| Dataset Splits | Yes | we uniformly sample frames (at 20% and 40%) and perform affine transformations on the selected frames to distort them. and we omit 2, 4, 6, 8, and 10 (6.66%, 13.33%, 20%, 26.66%, and 33.33%) points out of total House landmark points (i.e., 30 points) from 40% (Figure 4) of frame sequences randomly. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud resources) were mentioned for running experiments. |
| Software Dependencies | No | No specific ancillary software dependencies (e.g., library or solver names with version numbers) are mentioned in the paper. |
| Experiment Setup | Yes | We set p = 0.7 and k = 7 as nearest neighbors to get the correct matchings. |