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HyperInvariances: Amortizing Invariance Learning
[article]
2022
arXiv
pre-print
Providing invariances in a given learning task conveys a key inductive bias that can lead to sample-efficient learning and good generalisation, if correctly specified. However, the ideal invariances for many problems of interest are often not known, which has led both to a body of engineering lore as well as attempts to provide frameworks for invariance learning. However, invariance learning is expensive and data intensive for popular neural architectures. We introduce the notion of amortizing
arXiv:2207.08304v1
fatcat:pryxbpkzybbv3dri7ed3l5uk2a