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..
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). |