Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors
Authors: Yazid Janati, Badr MOUFAD, Alain Durmus, Eric Moulines, Jimmy Olsson
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 31,797 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,745 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the versatility and effectiveness of our approach for a wide range of Bayesian inverse problems. The code is available at https://github.com/Badr-MOUFAD/dcps |
| Researcher Affiliation | Collaboration | Yazid Janati ,1 Badr Moufad ,1 Alain Durmus1 Eric Moulines1,3 Jimmy Olsson2 1 CMAP, Ecole polytechnique 2 KTH Royal Institute of Technology 3 MBZUAI |
| Pseudocode | Yes | The pseudo-code of the DCPS algorithm is in Algorithm 1. |
| Open Source Code | Yes | The code is available at https://github.com/Badr-MOUFAD/dcps |
| Open Datasets | Yes | For these imaging experiments, we use the FFHQ256 [23] and Image Net256 [14] datasets and the publicly available pre-trained models of [8] and [15]. |
| Dataset Splits | Yes | The UCY-student dataset was split int a train and a validation sets with 1450 and 140 trajectories respectively. |
| Hardware Specification | Yes | The memory consumption is measured by how many samples each algorithm can generate in parallel on a single 48GB L40S NVIDIA GPU for the Diffusion model trained on FFHQ [15]. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | For all the experiments we implement Algorithm 1. We use the same parameters K = 2, L = 3 and ΞΆ = 1 for all the experiments. For the number of Langevin steps, we set it to M = 50 and M = 500 (respectively) for the Gaussian mixture experiment and M = 5 for the imaging and trajectory inpainting experiments. |