Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Distributions on Manifolds with Free-Form Flows
Authors: Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Ullrich Köthe
NeurIPS 2024 | Venue PDF | 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) |