Notice: 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. Full accuracy metrics and methodology are described in [1].
Towards Robust Neural Networks via Close-loop Control
Authors: Zhuotong Chen, Qianxiao Li, Zheng Zhang
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 NUMERICAL EXPERIMENTS We test our proposed CLC-NN framework under various input data perturbations. Here we brieο¬y summarize our experimental settings, and we refer readers to Appendix B for the details. ... Result Summary: Table 1 and Table 2 show the results for both CIFAR-10 and CIFAR-100 datasets on some neural networks from both standard training and adversarial training respectively. |
| Researcher Affiliation | Collaboration | 1Department of Electrical & Computer Engineering, University of California, Santa Barbara, CA 93106 2Department of Mathematics, National University of Singapore, Singapore 3Institute of High Performance Computing, A*STAR, Singapore |
| Pseudocode | Yes | Algorithm 1: CLC-NN with the Pontryagin s Maximum Principle. |
| Open Source Code | Yes | 1A Pytorch implementation can be found in:https://github.com/zhuotongchen/ Towards-Robust-Neural-Networks-via-Close-loop-Control.git |
| Open Datasets | Yes | Result Summary: Table 1 and Table 2 show the results for both CIFAR-10 and CIFAR-100 datasets on some neural networks from both standard training and adversarial training respectively. |
| Dataset Splits | No | The paper mentions using CIFAR-10 and CIFAR-100 datasets for experiments but does not explicitly provide the training, validation, or test dataset split percentages or sample counts in the text. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions a 'Pytorch implementation' and the use of 'Adam' optimizer but does not provide specific version numbers for PyTorch or any other software libraries used. |
| Experiment Setup | Yes | We use Adam (Kingma & Ba, 2014) to maximize the Hamiltonian function (9) and keep the same hyperparameters (learning rate, maximum iterations) for each model against all perturbations. ... For the numerical results presented in the paper, we choose the maximum iteration that gives the best performance from one of [5, 10, 20, 30, 50]. |