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.
Defending Against Adversarial Attacks via Neural Dynamic System
Authors: Xiyuan Li, Zou Xin, Weiwei Liu
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,366 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,391 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 conduct experiments on the CIFAR-10 [16] and MNIST [17] datasets to evaluate the robustness of ASODE under different adversarial attacks. We follow the standard training, validation, and test splits in our experiments. Moreover, we compare the robustness of ASODE with ODE-Net [12], Tis ODE-Net [10], and SODEF [11]. |
| Researcher Affiliation | Academia | Xiyuan Li School of Computer Science Wuhan University EMAIL Xin Zou School of Computer Science Wuhan University EMAIL Weiwei Liu School of Computer Science Wuhan University EMAIL |
| Pseudocode | Yes | The pseudo code of ASODE algorithm is illustrated in Appendix B, which is from bringing the process above together. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | Yes | In this section, we conduct experiments on the CIFAR-10 [16] and MNIST [17] datasets to evaluate the robustness of ASODE under different adversarial attacks. |
| Dataset Splits | Yes | We follow the standard training, validation, and test splits in our experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions PyTorch as a framework but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | During the training of ASODE, we first train the neural ODE for 50 epochs, after which we fix hθ and train feθ for another 100 epochs. We set the parameters α1 = 0.1 and α2 = 0.05 when training ASODE. In the below, the best results are marked in bold. ... To facilitate fair comparison, we set T = 5 based on the original papers of the comparison models. |