Learning Graph Cellular Automata
Authors: Daniele Grattarola, Lorenzo Livi, Cesare Alippi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 grattd@usi.ch 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)). |