Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
Authors: Alberto Cabezas, Louis Sharrock, Christopher Nemeth
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of MFM (Algorithm 1) on two synthetic and two real data examples. Our method is benchmarked against four relevant methods. |
| Researcher Affiliation | Academia | Alberto Cabezas Department of Mathematics and Statistics Lancaster University, UK a.cabezasgonzalez@lancaster.ac.uk Louis Sharrock Department of Mathematics and Statistics Lancaster University, UK l.sharrock@lancaster.ac.uk Christopher Nemeth Department of Mathematics and Statistics Lancaster University, UK c.nemeth@lancaster.ac.uk |
| Pseudocode | Yes | Algorithm 1 Markovian Flow Matching |
| Open Source Code | Yes | Code to reproduce the experiments is provided at https://github.com/albcab/mfm. |
| Open Datasets | Yes | Our first real-world example considers the stochastic Allen Cahn model [7], used as a benchmark in [24], and described in Appendix C.5. One such model is the log-Gaussian Cox process (LGCP) introduced in [53], which is used to model the locations of 126 Scots pine saplings in a natural forest in Finland. See Appendix C.6 for full details. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits with percentages or sample counts. It refers to samples from the Markov chain for training, but not fixed dataset splits. |
| Hardware Specification | Yes | All experiments are run on an NVIDIA V100 GPU with 32GB of memory. |
| Software Dependencies | No | Code for the numerical experiments is written in Python with array computations handled by JAX [11]. The paper mentions JAX but does not specify a version number for JAX or Python, nor does it list other software dependencies with specific version numbers. |
| Experiment Setup | Yes | For this experiment, all methods use N = 128 parallel chains for training and 128 hidden dimensions for all neural networks. Methods with a MALA kernel use a step size of 0.2, and methods with splines use 4 coupling layers with 8 bins and range limited to [ 16, 16]. |