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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Information-Theoretic Local Minima Characterization and Regularization
Authors: Zhiwei Jia, Hao Su
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are performed on CIFAR-10, CIFAR-100 and Image Net for various network architectures. |
| Researcher Affiliation | Academia | 1University of California, San Diego. Correspondence to: Zhiwei Jia <EMAIL>, Hao Su <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Regularized Gradient Descent |
| Open Source Code | Yes | The code is available at https://github.com/Sean Jia/Info MCR. |
| Open Datasets | Yes | Experiments are performed on CIFAR-10, CIFAR-100 and Image Net for various network architectures. |
| Dataset Splits | Yes | select β by validation via a 45k/5k training data split for each of the network architecture & dataset pair. |
| Hardware Specification | Yes | We benchmark WRN-18 on the down-sampled Image Net classification dataset with 2 Nvidia 2080 Ti GPUs and a batch size of 128. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' for implementation details, but it does not specify version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | For the three hyper-parameters α, β, M in our proposed Algorithm 1, we find α and M quite robust and manually set α = 0.0001, M = 8 in all experiments and select β by validation via a 45k/5k training data split for each of the network architecture & dataset pair. In specific, we consider β {1, 5, 10, 20, 30, 40, 50, 75, 100}. We keep all the other training hyper-parameters, schemes as well as the setup identical to those in their original paper whenever possible (details in Appendix E). |