Poincare embeddings for Learning Hierarchical RepresentationsTatsuya Shirakawa
?
This document summarizes a paper that introduces Poincaré embeddings, which use hyperbolic geometry to learn hierarchical representations of data. Poincaré embeddings represent data in the Poincaré disk model of hyperbolic space, which allows hierarchical structures to be automatically captured. The paper proposes optimizing the embeddings with Riemannian stochastic gradient descent. Evaluation on word taxonomy and graph link prediction tasks shows Poincaré embeddings achieve better results than previous methods at reconstructing the underlying hierarchical structures.
Poincare embeddings for Learning Hierarchical RepresentationsTatsuya Shirakawa
?
This document summarizes a paper that introduces Poincaré embeddings, which use hyperbolic geometry to learn hierarchical representations of data. Poincaré embeddings represent data in the Poincaré disk model of hyperbolic space, which allows hierarchical structures to be automatically captured. The paper proposes optimizing the embeddings with Riemannian stochastic gradient descent. Evaluation on word taxonomy and graph link prediction tasks shows Poincaré embeddings achieve better results than previous methods at reconstructing the underlying hierarchical structures.
9. nichiyou#4 塩鯖
B
を約分しなさいというときに (A-B) の約数をみつけたら
A
どうして A,B の公約数が見つかるんだろう。
[ おうちのひと向けの解説 ]
*
B
A
<1とする。
約分したときの答えが
b
a
B
A
=
kb
ka
(k=公約数)
B=kb
A=ka
(A-B)=k(a-b)
とすると