Concepedia

Concept

embeddings

Parents

771

Publications

66.5K

Citations

2.5K

Authors

579

Institutions

About

Embeddings is a methodological approach and concept in data representation that maps high-dimensional, often discrete data points (such as words, graphs, or categories) into a lower-dimensional continuous vector space. This transformation is designed to capture and preserve meaningful relationships and structural properties inherent in the original data, enabling quantitative analysis, facilitating similarity comparisons, and serving as an effective input representation for various computational models and machine learning algorithms.

Top Authors

Rankings shown are based on concept H-Index.

ZL

Tsinghua University

WH

Nanjing University

MC

University of Southern California

ZS

Nanjing University

MS

Tsinghua University

Top Institutions

Rankings shown are based on concept H-Index.

Tsinghua University

Beijing, China

Peking University

Beijing, China