Graph neural networks and non-commuting operators
Authors: Mauricio Velasco, Kaiying O'Hare, Bernardo Rychtenberg, Soledad Villar
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our theoretical results with simple experiments on synthetic and realworld data. |
| Researcher Affiliation | Collaboration | Mauricio Velasco Departamento de Informática Universidad Católica del Uruguay Montevideo, Uruguay EMAIL Kaiying O Hare Departament of Applied Mathematics and Statistics Johns Hopkins University Baltimore, Maryland EMAIL Bernardo Rychtenberg Departamento de Informática Universidad Católica del Uruguay Montevideo, Uruguay EMAIL Soledad Villar Departament of Applied Mathematics and Statistics Johns Hopkins University Baltimore, Maryland EMAIL |
| Pseudocode | No | The paper describes a training procedure but does not provide it in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | 1Code available: https://github.com/Kkylie/Gt NN_weighted_circulant_graphs and https://github.com/mauricio-velasco/operator Networks |
| Open Datasets | Yes | We use the publicly available Movie Lens 100k database, a collection of movie ratings given by a set of 1000 users [39] to 1700 movies. |
| Dataset Splits | No | We train our model with 800 training data I and test it on 200 testing data Itest. The paper does not explicitly mention a validation split. |
| Hardware Specification | Yes | Running these experiments took a few hours on a regular laptop (just CPU). |
| Software Dependencies | No | The paper mentions using ADAM for training but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We use MSE loss, and use ADAM with learning rate 0.01, β1 = 0.9 and β2 = 0.999 to train our models. For all four models, we set the non-commutative polynomial h(T1, T2) to be any polynomial of degree at most d = 3. |