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.
Convergence properties of the KMeans algorithm
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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.