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First-order ANIL learns linear representations despite misspecified latent dimension

In the limit of an infinite number of tasks, first-order ANIL with a linear two-layer network architecture provably learns linear shared representations despite having a width larger than the dimension of the shared representations.

Oğuz Kaan Yüksel, Etienne Boursier, Nicolas Flammarion