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.
CerDEQ: Certifiable Deep Equilibrium Model
Authors: Mingjie Li, Yisen Wang, Zhouchen Lin
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 21,153 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,667 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 try to demonstrate the effectiveness of our proposed method for training a certified deep equilibrium model and the advantages of Cer DEQ via experiments on CIFAR-10 and Tiny Image Net. |
| Researcher Affiliation | Academia | 1Key Lab. of Machine Perception (Mo E), School of Artificial Intelligence, Peking University. 2Institute for Artificial Intelligence, Peking University. 3Peng Cheng Laboratory. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks labeled as such. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We adopt two datasets, CIFAR-10 and Tiny Image Net, to demonstrate the effectiveness of our method. |
| Dataset Splits | No | The paper mentions training epochs ('70 epochs in total on CIFAR-10') and the use of datasets (CIFAR-10, Tiny Image Net), but it does not specify explicit training, validation, and test dataset splits (e.g., 80/10/10 split, or sample counts for each split). |
| Hardware Specification | Yes | All the experiments are run on the Py Torch platform with GTX1080Ti. |
| Software Dependencies | No | The paper mentions 'Py Torch platform' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | Other hyperparameters for the experiments can be found in Appendix E. Table 10. The hyper-paramters for Cer DEQ s certified training for CIFAR-10 and Tiny Image Net. |