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.
ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks
Authors: Yuezhu Xu, S Sivaranjani
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 30,233 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,168 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 | We implement our algorithms on randomly generated neural networks and ones trained on the MNIST dataset. |
| Researcher Affiliation | Academia | Yuezhu Xu Edwardson School of Industrial Engineering Purdue University West Lafayette, IN, USA EMAIL S. Sivaranjani Edwardson School of Industrial Engineering Purdue University West Lafayette, IN, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 ECLips E and ECLips E-Fast |
| Open Source Code | Yes | https://github.com/Yuezhu Xu/ECLips E |
| Open Datasets | Yes | We implement our algorithms on randomly generated neural networks and ones trained on the MNIST dataset. |
| Dataset Splits | No | The paper does not specify validation dataset splits for either randomly generated networks or the MNIST dataset. |
| Hardware Specification | Yes | All experiments are implemented on a Windows laptop with a 12-core CPU with 16GB of RAM. |
| Software Dependencies | No | The paper does not specify versions for software dependencies such as programming languages or libraries used for implementation (e.g., Python, PyTorch). |
| Experiment Setup | Yes | For training on the dataset MNIST, ... We train neural networks using the SGD optimizer with a learning rate of 0.01 and momentum of 0.9... For randomly generated networks, ... we scale the weights on each layer such that the is norm randomly chosen in [0.4, 1.8], following a uniform distribution. |