A Fast Optimistic Method for Monotone Variational Inequalities
Authors: Michael Sedlmayer, Dang-Khoa Nguyen, Radu Ioan Bot
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically validate our algorithm we investigate a two-player matrix game with mixed strategies of the two players. Concluding, we show promising results regarding the application of f OGDA-VI to the training of generative adversarial nets. |
| Researcher Affiliation | Academia | 1Research Network Data Science, University of Vienna, Vienna, Austria 2Faculty of Mathematics, University of Vienna, Vienna, Austria. |
| Pseudocode | Yes | Algorithm 1 f OGDA-VI |
| Open Source Code | Yes | In the following we report the code of the wrapper for the f OGDA-VI optimiser written using the Py Torch (Paszke et al., 2019) framework. |
| Open Datasets | Yes | apply our proposed algorithm f OGDA-VI to train Res Net architectures on the CIFAR-10 dataset. |
| Dataset Splits | No | The paper mentions using the CIFAR-10 dataset but does not explicitly provide details about train, validation, and test splits or how they were derived/used. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the Py Torch framework but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | In Table 4 we list the hyperparameters that were used for f OGDA-VI to obtain the results on CIFAR-10. Batch size = 128 Iterations = 500,000 Adam β1 = 0.0 Adam β2 = 0.9 Update ratio D/G = 5 Learning rate for discriminator = 1e-4 Learning rate for generator = 1e-4 f OGDA α = 100 f OGDA n = 1000 |