Trans-Dimensional Generative Modeling via Jump Diffusion Models
Authors: Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Thomas Rainforth, Arnaud Doucet
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our approach on molecular and video datasets of varying dimensionality, reporting better compatibility with test-time diffusion guidance imputation tasks and improved interpolation capabilities versus fixed dimensional models that generate state values and dimensions separately. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Oxford, UK 2 Department of Computer Science, University of British Columbia, Vancouver, Canada 3CNRS ENS Ulm, Paris, France |
| Pseudocode | Yes | Algorithm 1: Sampling the Generative Process |
| Open Source Code | Yes | Our code is available at https://github.com/andrew-cr/jump-diffusion |
| Open Datasets | Yes | We model the QM9 dataset [35, 36] of 100K varying size molecules. |
| Dataset Splits | No | We train on the 100K molecules contained in the QM9 training split. |
| Hardware Specification | Yes | Training a model requires approximately 7 days on a single GPU which was done on an Academic cluster. |
| Software Dependencies | No | The paper mentions software like 'Adam optimizer', 'RDKit', 'UNet', 'Transformer', and 'EGNN' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train our model for 1.3 million iterations at a batch size of 64. We use the Adam optimizer with learning rate 0.00003. We also keep a running exponential moving average of the network weights that is used during sampling as is standard for training diffusion models [2, 3, 16] with a decay parameter of 0.9999. |