Hyperbolic Neural Networks Hyperbolic Neural Networks
Use hyperbolic space instead of Euclidean space for embedding data with a latent hierarchical structure
Use hyperbolic space instead of Euclidean space for embedding data with a latent hierarchical structure image source: http://inspirehep.net/record/1355197/plots The volume of a ball grows exponentially with its radius!
Use hyperbolic space instead of Euclidean space for embedding data with a latent hierarchical structure image source: http://inspirehep.net/record/1355197/plots Image source: http://prior.sigchi.org The volume of a ball grows Similarly as for a tree: the number of nodes exponentially with its grows exponentially with the tree depth! radius!
Use hyperbolic space instead of Euclidean space for embedding data with a latent hierarchical structure Hot topic in ML since Poincaré Embeddings for Learning Hierarchical Representations, Nickel & Kiela, (NIPS 2017) Image source: http://prior.sigchi.org
Poincaré Ball Poincaré Ball
Poincaré Ball Poincaré Ball
Poincaré Ball Poincaré Ball
Our contributions Our contributions exp ( v ) x Image sources: stackexchange.com , wikipedia.org
Our contributions Our contributions
Our contributions Our contributions
Our contributions Our contributions
Our contributions Our contributions
Riemannian Optimization Riemannian Optimization Both Euclidean and hyperbolic parameters Riemannian SGD: exp ( v ) D c R n x x ← exp (− η ∇ L ), x ∈ x x c Riemannian gradient: 2 c 2 ∇ R L = (1/ λ ) ∇ L , conformal factor λ c = x x x x 1 − c ∥ x ∥ 2 Image source: stackexchange.com
Experiments Experiments
Experiments Experiments All word and sentence embeddings have dimension 5.
Experiments Experiments
Experiments Experiments
THANK YOU! THANK YOU! Please visit our website: hyperbolicdeeplearning.com Octavian Ganea is currently looking for postdoctoral positions!
Matrix-vector multiplication We define: Nice properties:
Matrix-vector multiplication When the curvature c goes to zero, it recovers the usual matrix multiplication! ⊗ lim ( x ) = M Mx c c →0
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