Learning Distributions on Manifolds with Free-Form Flows
Authors: Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Ullrich Köthe
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide our code at https://github.com/vislearn/FFF. ... M-FFF consistently matches or outperforms previous single-step methods specialized to specific manifolds. It is typically two orders of magnitude faster than multi-step methods based on diffusion or flow matching, achieving better likelihoods in several experiments. ... We now demonstrate the practical performance of manifold free-form flows on various manifolds. We choose established experiments to ensure comparability with previous methods |
| Researcher Affiliation | Academia | Computer Vision and Learning Lab, Heidelberg University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide our code at https://github.com/vislearn/FFF. |
| Open Datasets | Yes | We use four established datasets compiled by Mathieu and Nickel [2020] for density estimation on S2: Volcanic eruptions [NGDC/WDS, 2022b], earthquakes [NGDC/WDS, 2022a], floods [Brakenridge, 2017] and wildfires [EOSDIS, 2020]. |
| Dataset Splits | Yes | We follow previous works and use a dataset split of 80% for training, 10% for validation and 10% for testing. |
| Hardware Specification | Yes | one training run takes approximately 7 hours on a NVIDIA A40. ... Each model trained around 20h on a compute cluster using a single NVIDIA A40. ... takes 2.5 to 3 hours on a NVIDIA Ge Force RTX 2070 graphics card. |
| Software Dependencies | No | The paper mentions software packages like Py Torch, Py Torch Lightning, Numpy, Matplotlib, Pandas, and geomstats but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | In all cases, we selected hyperparameter using the performance on the validation data. ... (Table 9: Hyperparameter choices for the rotation experiments. Layer type Res Net, Residual blocks 2, Inner depth 5, Inner width 512, Activation Re LU, βx R 500, βz R 0, βU 10, βP 10, Latent distribution uniform, Optimizer Adam, Learning rate 5 10 3, Scheduler Exponential w/ γ = 1 10 5, Gradient clipping 1.0, Weight decay 3 10 5, Batch size 1,024, Step count 585,600, #Repetitions 3) |