Euclidean embeddings of finite metric spaces

By Hiroshi Maehara. 2013

29 citations recorded by the source corpus.

Subfields: Applied Mathematics, Discrete Mathematics and Combinatorics, Geometry and Topology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Xia Hu
2. Qingquan Song
3. Been Kim
4. Haifeng Jin

In Discrete Mathematics and Combinatorics

1. Xia Hu
2. Qingquan Song
3. Haifeng Jin

In Geometry and Topology

1. Xia Hu
2. Qingquan Song
3. Been Kim
4. Haifeng Jin

Citations in the corpus

• Auto-Keras: An Efficient Neural Architecture Search System (2018, background): “Any finite metric space distance can be isomet-rically embedded into Euclidean space by changing the scale of the distance measurement [27].”
• Visualizing and Measuring the Geometry of BERT (2019, background): “(See [12] for an independent version of this theorem.”
• Efficient Neural Architecture Search with Network Morphism (2018, background): “Any finite metric space distance can be isometrically embedded into Euclidean space by changing the scale of the distance measurement [19].”
• Auto-Keras: Efficient Neural Architecture Search with Network Morphism (2018, background): “Any finite metric space distance can be isometrically embedded into Euclidean space by changing the scale of the distance measurement (Maehara, 2013).”