SE(3) Equivariant Augmented Coupling Flows
Authors: Laurence Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When trained on the DW4, LJ13, and QM9-positional datasets, our flow is competitive with equivariant continuous normalizing flows and diffusion models, while allowing sampling more than an order of magnitude faster. [...] 4 Experiments |
| Researcher Affiliation | Academia | Laurence I. Midgley University of Cambridge lim24@cam.ac.uk Vincent Stimper Max Planck Institute for Intelligent Systems University of Cambridge vs488@cam.ac.uk Javier Antorán University of Cambridge ja666@cam.ac.uk Emile Mathieu University of Cambridge ebm32@cam.ac.uk Bernhard Schölkopf Max Planck Institute for Intelligent Systems bs@tue.mpg.de José Miguel Hernández-Lobato University of Cambridge jmh233@cam.ac.uk |
| Pseudocode | Yes | Algorithm 1: Flow block f [...] Algorithm 2: Joint density evaluation |
| Open Source Code | Yes | Code: https://github.com/lollcat/se3-augmented-coupling-flows |
| Open Datasets | Yes | For DW4 and LJ13 we use a training and test set of 1000 samples following Satorras et al. [2021a]. [...] For QM9-positional we use a training, validation and test set of 13831, 2501 and 1,813 samples respectively also following Satorras et al. [2021a]. |
| Dataset Splits | Yes | For QM9-positional we use a training, validation and test set of 13831, 2501 and 1,813 samples respectively also following Satorras et al. [2021a]. |
| Hardware Specification | Yes | Importantly, sampling and density evaluation of the E-ACF on an A100 GPU takes roughly 0.01 seconds. [...] All runs used an Ge Force RTX 2080Ti GPU . [...] DW4 was run using a Ge Force RTX 2080Ti GPU and LJ13 with a V-4 TPU. |
| Software Dependencies | No | The paper mentions software like "Adam optimizer", "dopri5 ODE solver", and custom EGNN implementation, but does not provide specific version numbers for any of these. |
| Experiment Setup | Yes | All flow models using 12 blocks (see Alg. 1 for the definition of a block). For the noise scale of the augmented variables sampled from N(a; x, η2I), we use η = 0.1 for both the base of the flow, and augmented target distribution. [...] We use a cosine learning rate schedule, that is initialised to a value of 0.00002, peaks at 0.0002 after 30 epochs, and then decays back to a final learning rate of 0.00002 for DW4 and LJ13. [...] We use a batch size of 32 for all experiments, and 100, 400 and 800 epochs of the datasets for DW4, LJ13 and QM9-positional respectively. |