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On the hardness of learning under symmetries
We study the problem of learning equivariant neural networks via gradient descent.
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
We prove a Poincar\'e inequality for graphon signals and show that complements of node subsets satisfying this inequality are unique sampling sets for Paley-Wiener spaces of graphon signals.
Training invariances and the low-rank phenomenon: beyond linear networks
The implicit bias induced by the training of neural networks has become a topic of rigorous study.
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