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].
ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks
Authors: Yuezhu Xu, S Sivaranjani
NeurIPS 2024 | Venue PDF | LLM Run Details
| 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. |