Holographic and other Point Set Distances for Machine Learning

Anonymous

Sep 27, 2018 ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: We introduce an analytic distance function for moderately sized point sets of known cardinality that is shown to have very desirable properties, both as a loss function as well as a regularizer for machine learning applications. We compare our novel construction to other point set distance functions and show proof of concept experiments for training neural networks end-to-end on point set prediction tasks such as object detection.
  • Keywords: point set, set, permutation-invariant, loss function
  • TL;DR: Permutation-invariant loss function for point set prediction.
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