The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium
Authors: Ian Gemp, Charlie Chen, Brian McWilliams
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically we demonstrate that this resulting algorithm is able to solve a variety of SGEP problem instances including a large-scale analysis of neural network activations. |
| Researcher Affiliation | Industry | imgemp@deepmind.com Charlie Chen ccharlie@deepmind.com Brian Mc Williams Google Research Z urich, Switzerland bmcw@google.com |
| Pseudocode | Yes | Algorithm 1 Deterministic / Full-batch γ-Eigen Game; Algorithm 2 Stochastic γ-Eigen Game |
| Open Source Code | Yes | A Jax implementation is available at github.com/deepmind/eigengame. |
| Open Datasets | Yes | We replicate a synthetic experiment from scikit-learn(Pedregosa et al., 2011) and compare Algorithm 2 to several approaches... loading minibatches of CIFAR-10 images, running them through a deep convolutional network, harvesting the activations, and then passing them to our distributed γ-Eigen Game solver. |
| Dataset Splits | No | The paper mentions using minibatches and datasets like CIFAR-10, but it does not specify explicit training, validation, or test dataset splits (e.g., 80/10/10 percentages or sample counts). |
| Hardware Specification | Yes | Figure 4 demonstrates our approach (parallelized over 8 TPU chips) |
| Software Dependencies | No | The paper mentions using 'Jax', 'Scipy's linalg.eigh(A, B)', and 'scikit-learn', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Hyperparameters are listed in Appx. H. |