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.
Spherical S liced- W asserstein
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.