CerDEQ: Certifiable Deep Equilibrium Model
Authors: Mingjie Li, Yisen Wang, Zhouchen Lin
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |