Claire Vernade, Ian Gemp, Brian Mcwilliams, Thore Graepel
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| gauravdhama/eigengame_deepmind | pwc_unofficial | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| calc_numpy_eig | Ran | gauravdhama/eigengame_deepmind/pca_eigengame.py code served (permissive licence) · get_code("b41e6c7b8ccd625f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm-which combines elements from Oja's rule with a generalized Gram-Schmidt orthogonalization-is naturally decentralized and hence parallelizable through message passing. We demonstrate the scalability of the algorithm with experiments on large image datasets and neural network activations. We discuss how this new view of PCA as a differentiable game can lead to further algorithmic developments and insights.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.00554")
get_code_for_paper("2010.00554")
have("2010.00554")
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