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..
Energy-guided Entropic Neural Optimal Transport
Authors: Petr Mokrov, Alexander Korotin, Alexander Kolesov, Nikita Gushchin, Evgeny Burnaev
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In practice, we validate its applicability in toy 2D and image domains. |
| Researcher Affiliation | Academia | 1Skolkovo Institute of Science and Technology, Moscow, Russia 2Artificial Intelligence Research Institute, Moscow, Russia EMAIL |
| Pseudocode | Yes | Algorithm 1: Entropic Optimal Transport via Energy-Based Modelling |
| Open Source Code | Yes | Our code is available at: https: //github.com/Petr Mokrov/Energy-guided-Entropic-OT |
| Open Datasets | Yes | The AFHQ dataset is taken from the Star GAN v2 (Choi et al., 2020) github: https://github.com/clovaai/stargan-v2. For generating Colored MNIST dataset, we make use of the code, provided by the authors of (Gushchin et al., 2023). |
| Dataset Splits | No | The paper mentions drawing samples from source and target distributions (e.g., 'Derive batches {xn}N n=1 = X P, {yn}N n=1 = Y Q of sizes N' in Algorithm 1, and 'For the source and target distributions, we choose P = N(0, ΣX) and Q = N(0, ΣY )' in D.2), but does not explicitly provide specific training, validation, or test dataset split percentages or sample counts. |
| Hardware Specification | Yes | The experiment was conducted on a single GTX 1080 Ti (D.1), The training takes approximately a day on 4 A100 GPUs. (D.3), It takes approximately one day on one V100 GPU (D.4). |
| Software Dependencies | No | The paper states 'Our code is written in Py Torch', but it does not specify version numbers for PyTorch or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | The hyperparameters of Algorithm 1 are as follows: K = 100, σ0 = 1, N = 1024, η = 0.05 for ε = 0.1 and η = 0.005 for ε = 0.001. (D.1) and MLP with Re LU activations and three hidden layers with 1024, 512 and 256 neurons, accordingly. The training hyperparameters are: K = 100, η = 0.008, σ0 = 1.0, N = 128. For both Cat Dog and Wild Dog experiments, we train our model for 11 epochs with a learning rate 10 4 which starts monotonically decreasing to 10 5 after the fifth epoch. (D.3). |