Polar Prototype Networks

ICLR 2019 Pascal MettesElise van der PolCees G. M. Snoek

This paper proposes a neural network for classification and regression, without the need to learn layout structures in the output space. Standard solutions such as softmax cross-entropy and mean squared error are effective but parametric, meaning that known inductive structures such as maximum margin separation and simplicity (Occam's Razor) need to be learned for the task at hand... (read more)

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