Dynamic Game Theoretic Neural Optimizer
Authors: Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |