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.
Solving Inverse Problems via Diffusion Optimal Control
Authors: Henry Li, Marcus Pereira
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 24,692 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,541 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 then evaluate our method against a selection of neural inverse problem solvers, and establish a new baseline in image reconstruction with inverse problems1. |
| Researcher Affiliation | Collaboration | Henry Li Yale University EMAIL Marcus Pereira Bosch Center for Artificial Intelligence EMAIL |
| Pseudocode | Yes | Algorithm 1 Diffusion Optimal Control Input: λ, T, y, x T Initialize ut, kt, Kt as 0 for t = 1 . . . T, {x t}T t=0 as uncontrolled dynamics for iter = 1 to num_iters do Vx, Vxx x0 log p(y|x0), 2 x0 log p(y|x0) Initialize derivatives of V (xt, t) for t = 1 to T do Compute kt, Kt, Vx, Vxx See Eqs. (13), (14) end for for t = T to 1 do xt 1 h(xt, λkt + Kt(xt x t)) Update xt 1 with new ut x t xt end for end for |
| Open Source Code | No | The authors will release code upon acceptance. |
| Open Datasets | Yes | We validate our results on the high resolution human face dataset FFHQ 256 256 Karras et al. [2019]. |
| Dataset Splits | Yes | To fairly compare between all models, all methods use the model weights from Chung et al. [2023a], which are trained on 49K FFHQ images, with 1K images left as a held-out set for evaluation. |
| Hardware Specification | Yes | Experiments can be run on any GPU A4000 or later. |
| Software Dependencies | No | The paper mentions 'Adam optimizer Kingma and Ba [2014]' and 'standard automatic differentiation libraries (e.g. torch.func.vjp)' but does not provide specific version numbers for these software dependencies, only the underlying algorithm for Adam. |
| Experiment Setup | Yes | Further hyperparameters can be found in Table 2. For the classifier p(y|x) in MNIST class-guided classification, we use a simple convolutional neural network with two convolutional layers and two MLP layers, trained on the entire MNIST dataset. |