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[R] Beyond Vector Spaces: Compact Data Representations Differentiable Weighted Graphs

[R] Beyond Vector Spaces: Compact Data Representations Differentiable Weighted Graphs

Paper: https://arxiv.org/abs/1910.03524 (NeurIPS 2019)

Code: https://github.com/stanis-morozov/prodige

The paper proposes an embedding layer based on weighted graph instead of vectors. Intuitively, this layer learns to represent concepts/words by their relation to other. Trains by backprop w.r.t. graph edges.

(Left) PRODIGE learned on a subset of MNIST. (Right) zoom-in of some clusters.

  • + Learns interpretable hierarchies from raw objects;
  • + The model is much smaller than typical vector embeddings;
  • The official code is CPU-only, it aint too fast

Interactive version of the plot above: https://neurips-anonymous.github.io/index.html

submitted by /u/justheuristic
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.