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.
Optimization for Amortized Inverse Problems
Authors: Tianci Liu, Tong Yang, Quan Zhang, Qi Lei
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 43,748 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,931 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 | In this section, we evaluate the performance of the proposed algorithm on three inverse problem tasks, including denoising, noisy compressed sensing, and inpainting. |
| Researcher Affiliation | Academia | 1 Purdue University, United States 2 Peking University, China 3 Michigan State University, United States 4 New York University, United States. |
| Pseudocode | Yes | Algorithm 1 AIPO algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | The two models are trained on the Celeb A dataset (Liu et al., 2015) |
| Dataset Splits | No | The paper mentions using samples from the Celeb A test set but does not provide explicit training, validation, or test splits used within their experimental setup. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU or GPU models used for the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam', 'Real NVP', and 'GLOW' but does not specify version numbers for them or any other software dependencies. |
| Experiment Setup | Yes | In all the experiments, we compare the algorithms with a prespecified λ, which is set to be 0.3, 0.5, 1.0, 1.5, 2.0, respectively. Our AIPO and the baseline algorithm with the MLE initialization require a solution to (4) on the NCS and inpainting tasks, where we run 500 iterations of projected gradient descent. Table 4. Hyper-parameters used in Amortized Optimization appeared Algorithm 2. Hyperparameter step size α iteration K target rate r min. step size δ0 min δmin,h δmin,h Value 0.05 40 0.05 Λ/20 4δ0 min 1 δ0 min/4 |