no code implementations • NeurIPS 2020 • Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant Rotskoff, Joan Bruna
Our method identifies mixed equilibria in high dimensions and is demonstrably effective for training mixtures of GANs.
no code implementations • 5 Feb 2019 • Grant Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden
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.
no code implementations • NeurIPS 2018 • Grant Rotskoff, Eric Vanden-Eijnden
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.