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..
Learning Graph Cellular Automata
Authors: Daniele Grattarola, Lorenzo Livi, Cesare Alippi
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now consider different experiments aimed at showcasing the capabilities of our GNCA architecture. We take inspiration from the literature on learning lattice-based CA to design three experimental settings with different goals. |
| Researcher Affiliation | Academia | Daniele Grattarola Università della Svizzera italiana EMAIL Lorenzo Livi University of Manitoba Cesare Alippi Università della Svizzera italiana Politecnico di Milano |
| Pseudocode | Yes | Algorithm 1: Pseudo-code for Boids [7]. |
| Open Source Code | Yes | Code is available online (see supplementary material). |
| Open Datasets | Yes | We consider several geometric graphs available in the Py GSP library [30] (BSD 3-Clause license) |
| Dataset Splits | Yes | We generate 300 trajectories for training, 30 for validation and early stopping, and 30 for testing the final performance of the GNCA. |
| Hardware Specification | No | The paper states 'See supplementary material' for compute and resource details, but these specifications are not present in the main paper. |
| Software Dependencies | No | The paper mentions the 'Py GSP library [30]' but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | We generate training examples for the model by sampling mini-batches of 32 random binary states [S(1), . . . , S(32)], S(k) Sn, and we train the GNCA by minimising the negative log-likelihood between the true successor states τ(S(k)) and the predicted next states τθ(S(k)). |