Variational Flow Matching for Graph Generation
Authors: Floor Eijkelboom, Grigory Bartosh, Christian Andersson Naesseth, Max Welling, Jan-Willem van de Meent
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Cat Flow on one abstract graph generation task and two molecular generation tasks. In all cases, Cat Flow exceeds or matches performance of the current state-of-the-art. We evaluate Cat Flow in three sets of experiments. |
| Researcher Affiliation | Collaboration | Floor Eijkelboom Uv A-Bosch Delta Lab University of Amsterdam Grigory Bartosh AMLab University of Amsterdam Christian A. Naesseth Uv A-Bosch Delta Lab University of Amsterdam Max Welling Uv A-Bosch Delta Lab University of Amsterdam Jan-Willem van de Meent Uv A-Bosch Delta Lab University of Amsterdam |
| Pseudocode | Yes | Algorithm 1 Variational Flow Matching; Algorithm 2 Categorical Variational Flow Matching (Cat Flow); Algorithm 3 Gaussian Variational Flow Matching (all in Appendix B). |
| Open Source Code | Yes | We provide all code to the experiments. Moreover, all data is publically available, and will be provided in the repository, including data processing. |
| Open Datasets | Yes | We evaluate Cat Flow on two popular molecular generation benchmarks: QM9 [40] and ZINC250k [19]. |
| Dataset Splits | Yes | Furthermore, all data splits are kept the same as in [20] |
| Hardware Specification | Yes | All experiments were run on a single NVIDIA RTX 6000 and took about a day to run. |
| Software Dependencies | No | The paper mentions 'Adam W' as an optimizer and 'RDKit' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We report the hyperparameters here: Optimizer Adam W, Scheduler Cosine Annealing, Learning Rate 2e-4, Weight Decay 1e-12, EMA 0.999 (Appendix D.2, Table 3). |