The authors introduce sliced and Lloyd-based dispersion regularizers for hyperspherical embeddings, connect kernel dispersion objectives to maximum mean discrepancy, and show downstream gains in prototype classification and neural machine translation.
Riemannian adaptive optimization methods
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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$
The authors introduce sliced and Lloyd-based dispersion regularizers for hyperspherical embeddings, connect kernel dispersion objectives to maximum mean discrepancy, and show downstream gains in prototype classification and neural machine translation.