Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems.
Authors: Gabriel Cardoso, Yazid Janati el idrissi, Sylvain Le Corff, Eric Moulines
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems in a Bayesian setting. |
| Researcher Affiliation | Academia | Gabriel Cardoso* Ecole polytechnique IHU Liryc, Yazid Janati* Ecole polytechnique, Sylvain Le Corff Sorbonne Université, Eric Moulines Ecole polytechnique |
| Pseudocode | Yes | Algorithm 1: MCGdiff (σ = 0) |
| Open Source Code | Yes | The code for the experiments is available at https://github.com/gabrielvc/mcg_diff. |
| Open Datasets | Yes | Image datasets. Figure 3 shows samples of MCGdiff in different datasets (Celeb, Churches, Bedroom and Flowers)...We use a downsampling ratio of 4 for the CIFAR-10 dataset, 8 for both Flowers and Cats datasets and 16 for the others. The dimension of the datasets are recalled in table 4." Table 4 lists: "CIFAR-10", "Flowers", "Cats", "Bedroom", "Church", "Celeba HQ" |
| Dataset Splits | No | The paper mentions training procedures and uses datasets but does not explicitly provide specific training/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It does not mention any hardware specifications. |
| Software Dependencies | No | The paper mentions software like "Adam algorithm" and "normalizing flow" and "automatic differentiation libraries" but does not specify any version numbers for these software components or libraries. |
| Experiment Setup | Yes | The κ paramater of MCGdiff is κ2 = 10 4. We use 20 steps of DDIM for the numerical examples and for all algorithms. The sequence of {βs}1000 s=1 as a linearly decreasing sequence between β1 = 0.2 and β1000 = 10 4. The training procedure for variational inference used Adam algorithm with a learning rate of 10 3 and 200 iterations with Nnf = 10. |