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cs.LG2020★ 22 cited
Geometric compression of invariant manifolds in neural nets
Jonas Paccolat, Leonardo Petrini, Mario Geiger +2
We study how neural networks compress uninformative input space in models where data lie in dimensions, but whose label only vary within a linear manifold of dimension $d_\para…
cs.LG2020
How isotropic kernels perform on simple invariants
Jonas Paccolat, Stefano Spigler, Matthieu Wyart
We investigate how the training curve of isotropic kernel methods depends on the symmetry of the task to be learned, in several settings. (i) We consider a regression task, where t…