Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation
Authors: Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that on tiny models, our method produces comparable bounds compared to exact methods that cannot scale to slightly larger models; on larger models, our method efficiently produces tighter results than existing relaxed or naive methods, and our method scales to much larger practical models that previous works could not handle. |
| Researcher Affiliation | Collaboration | Zhouxing Shi1, Yihan Wang1, Huan Zhang2, Zico Kolter2,3, Cho-Jui Hsieh1 1University of California, Los Angeles 2Carnegie Mellon University 3Bosch Center for AI |
| Pseudocode | No | The paper describes its methodology in text and figures but does not include structured pseudocode or algorithm blocks with explicit labels like 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code is available at https: //github.com/shizhouxing/Local-Lipschitz-Constants. |
| Open Datasets | Yes | We conduct experiments on image datasets including MNIST [30], CIFAR-10 [27], and Tiny Imagenet [29]. |
| Dataset Splits | No | The paper mentions evaluating on a 'test set' but does not explicitly provide details about a validation dataset split (e.g., percentages, sample counts, or a specific citation for validation splits). |
| Hardware Specification | No | The paper does not explicitly provide specific hardware details (e.g., exact GPU/CPU models, memory, or cloud provider specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions tools like 'PyTorch' (Appendix A.1) but does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | No | The paper mentions timeout settings for baselines ('We set a timeout of 1000s for Lip MIP and Lip SDP, and 60s for Ba B.') and refers to prior work for training details ('We follow Jordan & Dimakis [24] and train several small models on a synthetic dataset.'), but it does not explicitly list specific hyperparameters such as learning rate, batch size, or optimizer settings for its own models. |