Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
A framework for inverse-designing such biases by constructing novel parametrizations and their associated symmetries is developed and it is shown how holomorphic functions make this construction and calculation simple and explicit.
Nicola Aladrah, Emanuele Ballarin, Matteo Biagetti et al.
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