Skip to search formSkip to main contentSkip to account menu

Kullback–Leibler divergence

Known as: Kl-divergence, KL-distance, Kullback divergence 
In probability theory and information theory, the Kullback–Leibler divergence, also called discrimination information (the name preferred by Kullback… 
Wikipedia (opens in a new tab)

Related topics

Related topics

50 relations
AutoencoderBayesian information criterionBiclusteringBoltzmann machine

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
Model Fusion with Kullback-Leibler Divergence
We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field… 
Highly Cited
2019
Highly Cited
2019
A Novel Kullback–Leibler Divergence Minimization-Based Adaptive Student's t-Filter
In this paper, in order to improve the Student's t-matching accuracy, a novel Kullback-Leibler divergence (KLD) minimization… 
2019
2019
Kullback–Leibler Divergence Between Multivariate Generalized Gaussian Distributions
The Kullback–Leibler divergence (KLD) between two multivariate generalized Gaussian distributions (MGGDs) is a fundamental tool… 
2018
2018
Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy
Complex simulator-based models usually have intractable likelihood functions, rendering the likelihood-based inference methods… 
Highly Cited
2017
Highly Cited
2017
Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem
Two geometrical structures have been extensively studied for a manifold of probability distributions. One is based on the Fisher… 
Highly Cited
2015
Highly Cited
2015
Electric Motor Fault Detection and Diagnosis by Kernel Density Estimation and Kullback–Leibler Divergence Based on Stator Current Measurements
This paper deals with the problem of fault detection and diagnosis of induction motor based on motor current signature analysis… 
Highly Cited
2014
Highly Cited
2014
Notes on Kullback-Leibler Divergence and Likelihood
The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that quantifies the proximity of two… 
Highly Cited
2010
Highly Cited
2010
Optimism in reinforcement learning and Kullback-Leibler divergence
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic… 
Highly Cited
2008
Highly Cited
2008
Similarity Measures for Text Document Clustering
Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful… 
Highly Cited
2005
Highly Cited
2005
Divergence measures and message passing
This paper presents a unifying view of messagepassing algorithms, as methods to approximate a complex Bayesian network by a… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)