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..
Dynamic Game Theoretic Neural Optimizer
Authors: Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiment, Table 2. Accuracy (%) of residual-based networks (averaged over 6 random seeds), Table 3. Accuracy (%) of inception-based networks (averaged over 4 random seeds), Figure 7. Our second-order method DGNOpt exhibits similar runtime (≈ 40%) and memory (≈ 30%) complexity compared to the second-order baseline EKFAC. |
| Researcher Affiliation | Academia | 1Center for Machine Learning 2School of Aerospace Engineering 3School of Electrical and Computer Engineering, Georgia Institute of Technology, USA. |
| Pseudocode | Yes | Algorithm 1 Dynamic Game Theoretic Neural Optimizer |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | Datasets and networks. We verify the performance of DGNOpt on image classification datasets... Specifically, we first consider residual-based networks... For larger datasets such as CIFAR10/100... For MNIST and SVHN... |
| Dataset Splits | No | The paper mentions common datasets like MNIST, SVHN, CIFAR10, and CIFAR100, but does not explicitly provide specific training/validation/test split percentages, sample counts, or explicit references to predefined standard splits within the main text. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments, such as GPU or CPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation or experimentation. |
| Experiment Setup | Yes | All networks use Re LU activation and are trained with 128 batch size. Other setups are detailed in Appendix E. |