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.
Improved techniques for deterministic l2 robustness
Authors: Sahil Singla, Soheil Feizi
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 22,695 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,141 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 | Using these methods, we significantly advance the state-of-the-art for standard and provable robust accuracies on CIFAR-10 (gains of +1.79% and +3.82%) and similarly on CIFAR-100 (+3.78% and +4.75%) across all networks. Code is available at https://github.com/singlasahil14/improved_l2_robustness. |
| Researcher Affiliation | Academia | Sahil Singla Department of Computer Science University of Maryland EMAIL Soheil Feizi Department of Computer Science University of Maryland EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/singlasahil14/improved_l2_robustness. |
| Open Datasets | Yes | We perform experiments under the setting of provably robust image classification on CIFAR-10 and CIFAR-100 datasets. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |
| Hardware Specification | Yes | All experiments were performed using 1 NVIDIA GeForce RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | All networks were trained for 200 epochs with initial learning rate of 0.1, dropped by a factor of 0.1 after 100 and 150 epochs. For adversarial training with curvature regularization, we use ρ = 36/255 (0.1411), γ = 0.5 for CIFAR-10 and ρ = 0.2, γ = 0.75 for CIFAR-100. |