Dynamic Conditional Optimal Transport through Simulation-Free Flows
Authors: Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that our method is competitive on several challenging conditional generation tasks, including an infinite-dimensional inverse problem. |
| Researcher Affiliation | Academia | Gavin Kerrigan Department of Computer Science University of California, Irvine gavin.k@uci.edu Giosue Migliorini Department of Statistics University of California, Irvine gmiglior@uci.edu Padhraic Smyth Department of Computer Science University of California, Irvine smyth@ics.uci.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for all of our experiments is available at https://github.com/GavinKerrigan/cot_fm |
| Open Datasets | Yes | The datasets moons, circles, swissroll are available through scikit-learn [Pedregosa et al., 2011]. ... The training and test datasets are generated following the same procedure as Hosseini et al. [2023]. |
| Dataset Splits | Yes | For all datasets, we generate a training set (i.e., samples from the target distribution) of 20, 000 samples and 1, 000 held-out validation samples for model selection. ... We generate a training set of 10, 000 (y, u) pairs using the procedure described above and a held-out validation set of 10, 000 (y, u) pairs for model selection. |
| Hardware Specification | No | The paper states: 'All models can be trained on a single GPU with less than 24 GB of memory, and our experiments were parallelized over 8 such GPUs on a local server.' This description lacks specific model numbers for the GPUs or CPUs, or more detailed specifications of the 'local server'. |
| Software Dependencies | No | The paper mentions software packages like 'scikit-learn [Pedregosa et al., 2011]', 'POT Python package [Flamary et al., 2021]', 'PyMC Python package [Abril-Pla et al., 2023]', 'FEniCS [Alnæs et al., 2015]', 'torchcfm package [Tong et al., 2023]', and 'neuraloperator package [Kovachki et al., 2021]'. However, it does not explicitly provide specific version numbers for these software dependencies within the text. |
| Experiment Setup | Yes | Table 4: Hyperparameter grid used for random search of the FM and COT-FM models on the 2D and Lotka-Volterra datasets. ... For all of the models in consideration, we fix the architecture to be have hidden_channels = 64, projection_channels = 256, and 32 Fourier modes. We train each model for 1500 epochs, and hyperparameters for each architecture are selected as follows: ... We use the Adam optimizer where we only tune the learning rate, leaving all other settings as their defaults in pytorch. |