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].
Graph Element Networks: adaptive, structured computation and memory
Authors: Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauza Villalonga, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Kaelbling
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We do a varied set of experiments to illustrate the diversity of applications of GENs. |
| Researcher Affiliation | Academia | 1CSAIL MIT, Cambridge, MA, USA 2Mechanical Engineering MIT, Cambridge, MA, USA. |
| Pseudocode | No | The paper mentions an "algorithm for training it from data" (Section 1) but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code can be found at https://github.com/FerranAlet/graph_element_networks. |
| Open Datasets | Yes | For training, we get ground-truth solutions using a FEM with a very dense mesh of 2502 nodes, computed with the program FEni CS (Alnæs et al., 2015). |
| Dataset Splits | No | The paper mentions training and evaluation on different sets, such as "trained on a set of houses ... and evaluated on a different set" (Section 4.1) and "measured on held-out data" (Section 4.2), but it does not provide specific percentages, absolute counts, or detailed methodologies for the train/validation/test splits in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions software like "FEniCS" (Section 4.1) but does not provide specific version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | Yes | We use a set of k2 nodes placed on a uniformly-spaced grid, and a number of message passing steps equal to the diameter of the graph, T = 2(k - 1). |