EigenGame: PCA as a Nash Equilibrium
Authors: Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the scalability of the algorithm with experiments on large image datasets and neural network activations. |
| Researcher Affiliation | Industry | Ian Gemp, Brian Mc Williams, Claire Vernade & Thore Graepel Deep Mind {imgemp,bmcw,vernade,thore}@google.com |
| Pseudocode | Yes | Algorithm 1 Eigen Game R-Sequential; Algorithm 2 Eigen Game R (Eigen Game update with ˆvi instead of R ˆvi) |
| Open Source Code | No | The paper does not contain an explicit statement that the source code for the methodology is being released, nor does it provide a link to a code repository. |
| Open Datasets | Yes | MNIST handwritten digits.; IMAGENET dataset |
| Dataset Splits | No | The paper mentions using 'training set' for MNIST and discusses 'held out runs' for synthetic data, but it does not provide specific percentages or counts for training, validation, and test splits, nor does it explicitly cite predefined splits for reproducibility. |
| Hardware Specification | Yes | Computing the top-32 principal components takes approximately nine hours on 32 TPUv3s. |
| Software Dependencies | No | The paper states, 'We implemented a data-and-model parallel version of Eigen Game in JAX (Bradbury et al., 2018),' but it does not provide a specific version number for JAX. |
| Experiment Setup | Yes | Learning rates were chosen from {10 3, . . . , 10 6} on 10 held out runs.; Sampling a mini-batch (of size 128) |