Reversible and irreversible bracket-based dynamics for deep graph neural networks
Authors: Anthony Gruber, Kookjin Lee, Nathaniel Trask
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section reports results on experiments designed to probe the influence of bracket structure on trajectory prediction and nodal feature classification. Additional experimental details can be found in Appendix B. In each Table, orange indicates the best result by our models, and blue indicates the best of those compared. |
| Researcher Affiliation | Collaboration | Anthony Gruber Center for Computing Research Sandia National Laboratories Albuquerque, NM. USA adgrube@sandia.gov Kookjin Lee School of Computing and Augmented Intelligence Arizona State University Tempe, AZ. USA kookjin.lee@asu.edu Nathaniel Trask School of Engineering and Applied Science University of Pennsylvania Philadelphia, PA. USA ntrask@seas.upenn.edu |
| Pseudocode | No | The paper describes methods and provides architectural diagrams but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | Yes | Code is available at the Github repository https://github.com/natrask/Bracket Graphs. |
| Open Datasets | Yes | Table 4 and Table 5 present results on common benchmark problems including the citation networks Cora [58], Citeseer [59], and Pubmed [60], as well as the coauthor graph, Coauthor CS [61], and the Amazon co-purchasing graphs, Computer and Photo [62]. |
| Dataset Splits | Yes | a Bayesian search is conducted using Weights and Biases [76] for each bracket and each dataset using a random 80/10/10 train/valid/test split with random seed 123. |
| Hardware Specification | Yes | The experiments are conducted on systems that are equipped with NVIDIA RTX A100 and V100 GPUs. |
| Software Dependencies | No | The paper mentions software like PYTHON, PYTORCH [71], CUDA, and TORCHDIFFEQ [16] but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | The networks are trained to reconstruct the node/edge features in mean absolute error (MAE) using the Adam optimizer [73]. The NODEs and metriplectic bracket use an initial learning rate of 10 4, while the other models use an initial learning rate of 10 3. The width of the hidden layers in the message passing encoder/decoder is 64, and the number of hidden features for nodes/edges is 32. The time integrator used is simple forward Euler. |