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 [1].
Efficient Graph Field Integrators Meet Point Clouds
Authors: Krzysztof Marcin Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Kumar Avinava Dubey, Deepali Jain, Tamas Sarlos, Snigdha Chaturvedi, Adrian Weller
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also perform exhaustive empirical evaluation, including on-surface interpolation for rigid and deformable objects (particularly for mesh-dynamics modeling), Wasserstein distance computations for point clouds, and the Gromov Wasserstein variant. |
| Researcher Affiliation | Collaboration | 1Google Research 2Columbia University 3Independent Researcher 4Haifa University 5Stanford University 6The Boston Dynamics AI Institute 7University of Cambridge 8The University of North Carolina at Chapel Hill 9The Alan Turing Institute. |
| Pseudocode | Yes | Algorithm 1 Fast Computation of Wasserstein Barycenter |
| Open Source Code | Yes | Our code is available at https://github.com/topographers/ efficient_graph_algorithms. |
| Open Datasets | Yes | We run tests on 120 meshes for 3D-printed objects with a wide range of sizes from the Thingi10k (Zhou & Jacobson, 2016) dataset (see Sec. C for details). |
| Dataset Splits | Yes | In each mesh, we randomly select a subset V V with |V | = 0.8|V| and mask out their vertex normals (set as zero vectors). |
| Hardware Specification | Yes | All experiments are run on a single computer with an i9-12900k CPU and 96GB memory. |
| Software Dependencies | No | For the baseline experiments, we use the implementation from the POT library (Flamary et al., 2021) for the GW-cg and FGW variants, and official implementation from (Xu et al., 2019) for the GW-prox variant. |
| Experiment Setup | Yes | For all our experiments, m = 16 random features, ϵ = 0.3, and the smoothing factor λ = 0.2 are chosen. |