Kullback-Leibler Divergence
By James M. Joyce. International Encyclopedia of Statistical Science, 2011
787 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Mechanics, Signal Processing
View Paper: https://www.semanticscholar.org/paper/3c7ab18a86779b2bfbb6c1fc86e9948acfb428d4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Abhishek Gupta
- Yew-Soon Ong
- Liang Feng
- Qiang Yang
- Chunyan Miao
- Dusist Niyato
- Wei Yang Bryan Lim
- Ying-Chang Liang
In Computational Mechanics
- Ying-Chang Liang
- Yew-Soon Ong
- Nguyen H. Tran
- Qiang Yang
- Chunyan Miao
- Dusist Niyato
- Laura E. Barnes
- Kamran Kowsari
In Signal Processing
- Chunyan Miao
- Ying-Chang Liang
- Yew-Soon Ong
- Qiang Yang
- Dusist Niyato
- Laura E. Barnes
- Kamran Kowsari
- Donald E. Brown
Citations in the corpus
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “Divergence [70] between local data and uniform distribution.”
- Revisiting Reverse Distillation for Anomaly Detection (2023, background): “KL is a Kullback-Leibler divergence [15], defined as:”
- Insights on Transfer Optimization: Because Experience is the Best Teacher (2018, methodology): “In this regard, a commonly used measure with convexity properties is the Kullback-Leibler divergence (DK L ) [27].”
- Text Classification Algorithms: A Survey (2019, methodology): “where KL(Pi|Qi) is the Kullback-Leibler divergence [102], which is calculated as: KL(Pi|Qi) = ∑ j pj|i log pj|i qj|i (66)”