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A mean-field analysis of two-player zero-sum games
Our method identifies mixed equilibria in high dimensions and is demonstrably effective for training mixtures of GANs.
Global convergence of neuron birth-death dynamics
Neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for the favorable training properties of "overparameterized" models.
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks
The performance of neural networks on high-dimensional data distributions suggests that it may be possible to parameterize a representation of a given high-dimensional function with controllably small errors, potentially outperforming standard interpolation methods.
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